Method for dynamically increasing plant root depth

ABSTRACT

According to one embodiment, a method for generating a dynamic watering plan that reduces water consumption requirements for vegetation is disclosed. An example method includes estimating root depth of vegetation watered by a watering system; determining an allowed water depletion threshold of the vegetation based on the root depth; determining a training watering plan to increase the root depth of the vegetation over time based on the root depth and the allowed water depletion threshold; and transmitting the training watering plan to a flow controller for execution by the watering system.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/712,736, filed Jul. 31, 2018, and entitled “Method for DynamicallyIncreasing Plant Root Depth,” and U.S. Provisional Application No.62/845,120, filed May 8, 2019, and entitled “Method for DynamicallyIncreasing Plant Root Depth,” the entireties of both of which areincorporated herein by reference for all purposes.

TECHNICAL FIELD

The technology described herein relates generally to landscape healthimprovement and irrigation management systems.

BACKGROUND

Conventional landscape sprinkler systems require irrigation schedules beset manually at the beginning of a watering season and are typically notadjusted based on vegetation, soil conditions, weather, or water supplyand demand forecasts. Additionally, homeowners typically lack knowledgeabout landscaping and sprinkler systems to create optimal irrigationschedules that minimize water consumption. This often results in anoverwatered lawn with wasted water as runoff. When grass is overwatered,it limits its root zone growth to the minimum depth required to besustainable. Longer roots are more desirable, as they are more droughtresistant and can survive with less water.

Recent advances in watering systems include smart watering systems.Smart watering systems control irrigation schedules of a sprinklersystem. This may include automatically updating irrigation schedulesbased on meteorological data. An example of a smart watering system isdisclosed in U.S. Patent Application Publication No. 2015/0319941,entitled “System and method for an improved sprinkler control system,”filed May 6, 2014, which is incorporated herein by reference for any andall purposes. Conventional smart watering systems also do not typicallyaddress root characteristics of the watered vegetation and moreover donot act to improve the vegetation characteristics to reduce waterconsumption.

The information included in this Background section of thespecification, including any references cited herein and any descriptionor discussion thereof, is included for technical reference purposes onlyand is not to be regarded subject matter by which the scope of theinvention as defined in the claims is to be bound.

SUMMARY

In one embodiment, a method for generating a watering plan forvegetation is disclosed. The method includes estimating by theprocessing element root depth of vegetation watered by a wateringsystem; determining by the processing element a training watering planto increase the root depth of the vegetation; and transmitting orstoring the training watering plan for execution by the watering system.

In another embodiment, a method for generating a watering plan for asprinkler system is disclosed. The method includes estimating currentroot depth value for vegetation watered by the sprinkler system;determining an optimal root depth value of the vegetation, wherein theoptimal root depth corresponds to a minimum watering volume for thevegetation to survive; and generating a training watering plan based onthe current root depth value and the optimal root depth value, whereinthe training plan reduces the water volume distributed by the sprinklersystem over time.

In a further embodiment, a watering system for vegetation is disclosed.The watering system includes a central controller configured to receive,process, and transmit information; a sensor for detecting at least oneof weather variables, soil moisture levels, or vegetationcharacteristics, wherein an output signal is sent to the centralcontroller when the sensor is activated; one or more databasescontaining information on watering history and vegetation specificationsand communicatively coupled to the central controller; and one or morecontrollers in communication with the central controller, thecontrollers configured to receive data from the central controller,wherein the one or more controllers open one or more sprinkler valvesbased on a watering schedule, wherein the watering schedule is selectedto increase a root depth for the vegetation.

In yet another embodiment, a method for generating a root developmentplan is disclosed. The method includes estimating root depth value;translating the estimated root depth value to an optimal root depthvalue; and determining a watering plan to dynamically adjust root depthvalues based upon the optimal root depth value.

In another embodiment, a method for generating a dynamic watering planthat reduces water consumption requirements for vegetation is disclosed.The method includes estimating, by a processing element, root depth ofvegetation watered by a watering system; determining, by the processingelement, an allowed water depletion threshold of the vegetation based onthe root depth; determining, by the processing element, a trainingwatering plan to increase the root depth of the vegetation over timebased on the root depth and the allowed water depletion threshold; andtransmitting the training watering plan to a flow controller forexecution by the watering system.

In another embodiment, a watering system for vegetation is disclosed.The watering system includes a server, a sensor, one or more databases,and one or more controllers. The server is configured to receive,process, and transmit information. The sensor detects at least one ofweather variables, soil moisture levels, or vegetation characteristics.The one or more databases contain information on watering history andvegetation specifications and are communicatively coupled to the server.The one or more controllers are in communication with the server and areconnected to at least one water outlet of a plurality of water outlets.The server includes a non-transitory computer readable media and isconfigured to execute instructions stored on the non-transitory computerreadable media. The instructions include estimating a root depth valuebased on at least one of the weather variables, soil moisture levels,vegetation characteristics, watering history, and vegetationspecifications; determining a water depletion threshold based on theroot depth value; estimating a water depletion rate based on at leastone of the weather variables, soil moisture levels, vegetationcharacteristics, watering history, and vegetation specifications; anddetermining a watering plan to increase the root depth value based atleast on the root depth value, the water depletion threshold, and thewater depletion rate.

In another embodiment, a method for improving landscape health of anarea is disclosed. The method includes receiving, by a processor,landscape health data specific to the area, wherein the landscape healthdata comprises vegetation and soil data, the vegetation data comprisingan estimated current root depth of the vegetation growing in the area;generating, by the processor, a recipe for a soil enhancement kit basedon the landscape health data, wherein the recipe includes at least onesoil additive for increasing the estimated current root depth of thevegetation; determining, by the processor, instructions for applying thesoil enhancement kit to the area, wherein the instructions includeinformation on timing, frequency, and duration of kit application; andtransmitting, by the processor, the at least one soil additive andinstructions to a customer.

In yet another embodiment, a method of generating a consumablemanufacturing and delivery schedule is disclosed. The method includesdetermining, by a processor, vegetation growth over time based on one ormore landscape characteristics; estimating, by the processor, consumablecharacteristics based on the vegetation growth over time, wherein theconsumable characteristics comprise a consumable type and amount and atiming of application; analyzing, by the processor, a supplier logisticsmodel to determine consumable supply and timing from acquisition orgeneration of the consumable to delivery; generating, by the processor,a manufacturing and delivery schedule based on the estimated consumablecharacteristics and the supplier logistics model; and utilizing by theprocessor, the manufacturing and delivery schedule to coordinatedelivery of the consumable to a user.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter. A moreextensive presentation of features, details, utilities, and advantagesof the present invention as defined in the claims is provided in thefollowing written description of various embodiments and implementationsand illustrated in the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example of a watering system.

FIG. 2A is a diagram illustrating an example of varying watering depthsfor an exemplary plant.

FIG. 2B is a diagram illustrating an example of changes in root depthover time as a watering plan is implemented by the watering system ofFIG. 1.

FIG. 3 is a flow chart illustrating a method to establish a wateringplan.

FIG. 4 is a flow chart illustrating a method for dynamically modifying awatering plan.

FIG. 5 is a flow chart illustrating an example of potential data inputsto generate a watering plan.

FIG. 6 is a flow chart illustrating a method to establish a wateringplan.

FIG. 7 is a flow chart illustrating a method to generate and apply asoil enhancement kit to improve vegetation health and reduce overallwater consumption.

FIG. 8 is a diagram illustrating an example of a deep root ecosystemcreated by the system of FIG. 1.

FIG. 9 is a flow chart illustrating a method of generating consumablemanufacturing and delivery schedules based on predicted consumablecharacteristics over time.

FIG. 10 is a flow chart illustrating a method for generating a wateringschedule or consumable recipe with reinforcement machine learning.

FIG. 11 is a flow chart illustrating a method for generating a wateringschedule or consumable recipe with supervised machine learning.

FIG. 12 is a simplified block diagram of a computing device that can beused by one or more components of the system of FIG. 1.

DETAILED DESCRIPTION

The present disclosure is generally related to systems and methods forimproving vegetation health for irrigated vegetation such as grass. Insome embodiments, vegetation health is improved by implementation of adynamic watering schedule implemented by a watering controller (e.g.,sprinkler controller) and/or the use of a soil enhancement kit. Byimproving vegetation health through the systems and methods of thepresent disclosure, the vegetation may utilize less water, be moresustainable, less susceptible to damage, and healthier.

Generally, vegetation, such as grass, perennials, and the like, areconsidered “healthy” when both the plant and the surrounding soilinclude characteristics that enable reduced water and/or increasedgrowth for the vegetation. Numerous factors may impact vegetationhealth, e.g., direct vegetation characteristics (e.g., type, height,cover, density, root depth, chlorophyll content, etc.), indirectvegetation characteristics, such as soil conditions (e.g., type,density, porosity, moisture level and depth, water capacity, nutrientcomposition, microbe composition, etc.), environmental conditions (e.g.,weather, sun exposure, landscape use, landscape age, etc.), historicalwatering patterns, and the like. To increase vegetation health, thepresent disclosure includes methods to encourage increased vegetationroot depth utilizing a dynamic watering plan implemented by sprinklersor other watering systems and/or a soil enhancement kit that can testand introduce beneficial factors into the soil.

In some embodiments herein, a system to automatically and dynamicallyadjust vegetation root depth is disclosed. The system may include acentral or cloud controller and one or more local controllers thatactivate sprinkler valves or other watering outlets based on schedulesfrom the cloud controller. The system analyzes historical watering data,local environmental variables, vegetation and soil specifications, andwater supply and demand forecasts to generate a root based watering planthat helps to increase the root depth of the vegetation over time. Thewatering plan may include an initial training phase and a subsequentgreen zone or sustainable watering phase. During the training phase, thesystem implements an initial watering schedule or training plan thatexposes the vegetation to low or sustainable watering volumes, e.g., aminimum overall watering value, in order to encourage root growth. Thewatering volumes applied by the system may generally decrease over timeas the vegetation becomes hardier and extends its roots, until asustainable or other selected watering volume is reached, with thesustainable watering volume being generally a minimal amount of waterthat the vegetation can survive at a desired “lushness” state.

In one embodiment, the system receives actual root depth measurements oruses a root depth analysis to determine or estimate a current root depthof the vegetation to be “trained” (e.g., vegetation watered by a localcontroller). Utilizing the actual or estimated root depth, the systemdetermines a watering plan to be implemented by the local controllerthat helps to gradually increase the root depth over time, adapting thevegetation so it can survive on a reduced or minimal watering volume,reducing the water consumption of the irrigated vegetation over time.During the training phase, the watering frequency may generally decreaseuntil the maximum, optimal, or other desired root depth is met. Afterthis training phase, during the green zone or sustainable wateringphase, the system can implement a green zone or sustainable wateringplan that reduces the overall water applied. It is noted that thesustainable watering plan may utilize a greater volume of water (e.g., awatering volume sufficient to engulf at least the lower portion of theroots) per watering event, but with less frequent watering events, orother predetermined watering plan based on desired vegetationcharacteristics. The green zone or sustainable watering plan may alsoaccount for weather, soil conditions, water supply and demand forecasts,user preferences, and the like to maintain the vegetation root depth.Conventional sprinkler controllers typically do not factor in rootdepth, nor cultivate vegetation to reach an optimal root depth,resulting in wasted water since the vegetation could survive on lesswater than is being distributed.

In some embodiments, the system estimates an initial root depth value byreceiving and analyzing input data relating to historical wateringschedules, vegetation characteristics, and soil conditions. Datarelating to historical watering schedules may include watering time,watering frequency, average delivered water volume, average amount ofwater used per square foot of vegetation, or the like. Vegetationcharacteristics may include vegetation type (e.g., species), height,cover, and density, among other data. In some instances, a direct rootmeasurement tool can also be used to determine vegetation root depth ata selected location or sample area, and that along with othercharacteristics and conditions can be used to extrapolate root depth forthe entire vegetation coverage area. Soil conditions may include soiltype, composition, density, porosity, and moisture level and depth,water capacity, among other data. This input data may be input by auser, by a third party database, and/or by sensors associated with thesystem 100. Using this data, the system estimates a root depth (e.g.,how far the roots extend into the soil) for the vegetation zone.

Utilizing the actual or estimated root depth value, the systemdetermines a root development plan to gradually encourage growth of theroots to an optimal, desired, or other predetermined root depth value.The optimal root depth may be based on a depth that would allow thevegetation to survive on a minimal total amount of water, but may alsotake into account a desired appearance by the user. For example, theminimum amount of water to ensure survival may result in “browner” orless vibrant vegetation and so the optimal root depth and wateringschedule may also take into account appearance such that the minimumwater for the vegetation might be higher than a minimum required foronly the survival of the vegetation.

The root development watering plan may include a training plan thatdecreases the overall water volume delivered to the vegetation overtime, causing roots to grow longer and extend deeper into the soil. Inother words, the total water volume delivered over a week of wateringevents is reduced, but some watering events may actually deliver morewater at a given time than previous watering events. Often, the trainingplan is selected to decrease the watering frequency gradually so as tonot “shock” or harm the vegetation, while still increasing the droughthardiness over time. The training phase ends when the root depth is atthe “green zone”, e.g., when the vegetation can survive with minimum orotherwise predetermined watering amount.

In several embodiments, the dynamic watering plan may incorporate both aroot depth threshold and an allowed water depletion threshold. Forexample, as the watering plan is implemented, the root depth dynamicallyadjusts (e.g., root depth increases). The water volume allotted perwatering event may correspond to the root depth (e.g., a larger volumeof water may be delivered during a watering event to saturate a largervolume of soil surrounding deeper roots) or otherwise change to allowlonger and deeper water seepage into the soil surrounding the roots.Therefore, in several embodiments, estimated and on-going changes inroot depth are factored into the dynamic watering plan. As anotherexample, water depletion of the soil can stress the roots, encouragingthe roots to grow more during each watering event. The amount of allowedwater depletion may be varied based on root length or depth. Forexample, longer roots may survive larger water depletion times andamounts than shorter roots. By increasing the allowed water depletion(and therefore stressing the roots), longer roots typically result. Insome embodiments, the allowed water depletion value is graduallyincreased to correspond with increasing root depth.

In several embodiments, the watering plan takes into account severalfactors related to vegetation health and soil conditions. For example,the factors may include one or more of root depth, chlorophyll content,crop coefficient, yield curve, soil water capacity, soil water content,soil water depletion rate, soil water depletion threshold, soiltemperature, and the like. A watering schedule may be determined toincrease root depth based on one or more of these factors. For example,based on root depth, the allowed water depletion value may be increasedby adjusting the watering plan to increase non-watering time (e.g.,training time) between watering events, resulting in less frequentwatering events, greater water depletion (e.g., longer stresses to theroots), and increased root depth. For example, water in the soil may beallowed to deplete by 50% or more, 60% or more, 70% or more, 80% ormore, 85% or more, 90% or more, 95% or more, or the like, before theoccurrence of a watering event. In some embodiments, the watering planexecuted by the system allows the roots to dry by at least 51% prior toinitiating a watering event.

The system may incorporate feedback data to dynamically vary the greenzone or ultimate watering plan and/or training plan. Feedback data mayinclude user preferences, pre-programmed specifications, root depthmeasurement data, computer learning, or other sources. In oneembodiment, feedback data can be provided by a user through a userdevice. For example, if after the green zone is reached (or at any pointduring the training plan), the user is dissatisfied with the colorand/or health of the vegetation (e.g., the user wants a lusher lawn),the user can provide feedback to modify the watering plan. In thisexample, the green zone or sustainable watering plan may be adjusted toincrease the watering threshold as compared to the watering thresholdimplemented based on the survival of the vegetation, without user input.This allows the vegetation to be a bit “greener” as more water isapplied, but still be selected to increase root depth. In yet anotherexample, the system can use pre-programmed user preferences to set orvary a watering plan. For example, a user can select a desiredvegetation coloration, and, using a camera, the system can determinewhether the vegetation color matches the selected preferences or whethermore/less watering is needed and adjust accordingly.

In some embodiments, the watering plan may incorporate health factors,such as products (e.g., soil additives), organisms, or the like, appliedto the soil and/or vegetation. For example, fertilizers (e.g., syntheticor organic), pesticides, compost, and the like may be applied to thesoil and/or vegetation. The health factor may also include changesintroduced indirectly by products applied to the soil, e.g., byproducts.For example, bacteria and/or fungi introduced as an applied product maymobilize different nutrients (e.g., phosphorus, nitrogen, potassium,etc.) within the soil. A user may input an applied product into thesystem, either directly or indirectly. For example, a user may scan abarcode on the product, upload an image of the product, manually enteringredients, and the like. Such soil additives and/or their associatedbyproduct(s) may alter root depth, soil water content, soil watercapacity, water depletion rate, and the like, and therefore the allowedwater depletion threshold may increase in a corresponding manner.

One or more soil additives may have an activation period, such as aperiod of time for the additive to induct a measurable or impactfuleffect on the surrounding environment (e.g., the plant and/or soil). Theactivation period may start when the soil additive is applied to thesoil or when a watering event occurs. For example, some soil additivesbecome soluble and mobilized with water, enabling the additives tosurround or come in contact with the roots. In this manner, the watermay act as a catalyst for the soil additives. The activation period maybe an inoculation period (e.g., a period of time for the additive toassociate with and/or be absorbed by the roots) or dissolution period,or the like. The activation period may indicate the time needed for thevegetation characteristics (e.g., root depth, etc.) and/or soilconditions (e.g., water content, water capacity, water depletion rate,etc.) to change after application of the applied product. Accordingly,in some embodiments, the watering plan may be gradually adjusted basedon the expected vegetation and soil changes resulting from the appliedproduct (and/or byproduct) and the activation period. In other words,the activation period may be one of a plurality of inputs factored intothe watering plan.

In some embodiments, a soil enhancement kit may be provided as or inaddition to the applied product with a system of the present disclosure.The soil enhancement kit may be used to supplement or as an alternativeto the root based watering plan to improve vegetation health. Forexample, the soil enhancement kit may be used to generate more desirablesoil conditions for optimal root growth. The soil enhancement kit mayinclude soil additives or agents that improve the condition of the soil,allowing for better growth and maintenance of vegetation growingtherein. For example, the soil enhancement kit may include one or moreof the following, in any combination: nutrients (e.g., nitrogen,phosphorus, potassium, etc.), fertilizer, soil conditioner, weedcontrol, wetting agent, bio stimulant, bacteria, fungi, plant hormones,and the like.

The amount and type of additives included in the kit may be varied basedon data related to the land, landscape, or watering area. For example,the landscape data may include information on current vegetation health(including, for example, current vegetation and soil conditions) andtarget vegetation health, local weather, seasonal changes, climate,historical watering patterns, current watering plan, and the like. Thesoil enhancement kit components may be dynamically generated by thesystem based on the specific inputs and needs of the respectivelandscape or other watering area, such that the components are tailoredto the user's specific landscape or other area, e.g., a user's backyard.Additionally, as the user applies the various components to the soil,the component application may be taken as an input to the wateringscheduler, to further update the watering program based on the expectedchanges to the soil due to the soil enhancement kit components. In otherwords, the soil enhancement kit variable can be an input to thecontroller to dynamically adjust the watering schedule to accommodatethe increasing or otherwise varying soil health of the soil surroundingthe roots, since vegetation planted in healthier or enhanced soil may beable to receive less water than vegetation (even with the same rootdepth) planted in less healthy soil.

Turning now to the figures, a system of the present disclosure will bediscussed in more detail. FIG. 1 is a block diagram illustrating anexample of a watering or irrigation system 100. The system 100 includesone or more irrigation system controllers 102, 112 that control one ormore fluid delivery devices, e.g., sprinkler valves, irrigation driplines, and the like. The irrigation controllers 102, 112 are incommunication with one or more central controllers 104, which in turnmay be in communication with one or more user devices 108 a-108 n, via anetwork 114. At least one of the irrigation controllers 102, 112 and/orthe central controller 104 may be in communication with one or moresensors 106 that detect one or more weather variables (e.g.,precipitation, humidity, atmospheric pressure, or the like), soilconditions (e.g., moisture levels), vegetation characteristics (e.g.,height, root depth, cover, etc.), or the like, as discussed in moredetail below. The one or more sensors 106 may include, for example,thermal sensors (e.g., a thermometer), pressure sensors (e.g., abarometer), motion sensors, visual sensors (e.g., a camera), or thelike. At least one of the irrigation controllers 102, 112 and/or thecentral controller 104 and/or the user devices 108 a-n, is incommunication with one or more database(s) 116 that provide additionaldata to the system 100, such as, for example, weather data, soil data,and the like. Each of the various components of the watering system 100may be in communication directly or indirectly with one another, such asthrough the network 114. In this manner, each of the components cantransmit and receive data from other components in the system. In manyinstances, the central controller 104 may act as a go between for someof the components in the system 100.

The network 114 may be substantially any type or combination of types ofcommunication systems for transmitting data either through a wired orwireless mechanism (e.g., WiFi, Ethernet, Bluetooth, cellular data, orthe like). In some embodiments, certain components in the wateringsystem 100 may communicate via a first mode (e.g., Bluetooth) and othersmay communicate via a second mode (e.g., WiFi). Additionally, certaincomponents may have multiple transmission mechanisms and be configuredto communicate data in two or more manners. The configuration of thenetwork 114 and communication mechanisms for each of the components maybe varied as desired and based on the needs of a particularconfiguration or property.

The irrigation system controllers 102, 112 control water flow to one ormore water outlets, such as sprinkler valves, irrigation lines,sprinkler heads, or the like. In one embodiment, the irrigation systemcontrollers 102, 112 are smart sprinkler controllers having processingelements, memory components, and control the operation of a plurality ofsprinkler valves in one or more watering zones for a particular propertyor area (e.g., residential property). An example of a sprinklercontroller that may be used with the system 100 can be found in U.S.Publication No. 2015/0319941 filed on May 6, 2014 and entitled“Sprinkler and Method for an Improved Sprinkler Control System,” whichis incorporated by reference herein in its entirety. The sprinklervalves may be electronically operated, such as one or more solenoidvalves that open and close a flow path to a sprinkler head. Theirrigation system controllers 102, 112 may include one or morecomponents, such as those shown in FIG. 12.

The central controller 104 or server is one or more computing devicesthat process and execute information. The central controller 104 mayinclude its own processing elements, memory components, and the like,and/or may be in communication with one or more external components(e.g., separate memory storage) (an example of computing elements thatmay be included in the central controller 104 is disclosed below withrespect to FIG. 12). The central controller 104 may also include one ormore server computers interconnected together via the network 114 orseparate communication protocol, such as through a cloud based computingplatform. The central controller 104 may host and execute a number ofthe processes performed by the system 100 and/or the irrigation systemcontrollers 102, 112.

The user devices 108 a, 108 n are various types of computing devices,e.g., smart phones, tablet computers, desktop computers, laptopcomputers, set top boxes, gaming devices, wearable devices, or the like.The user devices 108 a, 108 n provide output to and receive input from auser. For example, the central controller 104 may transmit one or morealerts to the user devices 108 a, 108 n to indicate informationregarding the irrigation system controllers 102, 112, fluid outlets,and/or the property being watered. The type and number of user devices108 a, 108 n may vary as desired.

The sensor 106 is substantially any type of device that can detect oneor more weather variables (e.g., precipitation, humidity, atmosphericpressure, temperature or the like), vegetation characteristics (e.g.,coloration, height, cover, density, root depth, or the like), and/orsoil characteristics (e.g., pH level, porosity, moisture level anddepth, or the like) and transmit an electrical signal. The sensor 106can include, for example, a thermometer, a barometer, a hygrometer, atensiometer, gypsum blocks, a camera, portable meters, or the like. Insome embodiments, the sensor 106 is a root depth sensor. In oneembodiment, the root depth sensor is a manually operated prong insertedinto the soil that may be analyzed by the user directly or an imagecaptured of the measuring tool that is used to indirectly analyze theoutput and determine a root depth value. The root depth value may beinput into the system 100 by the user (e.g., via a user device 108 a,108 n) or may be retrieved from the system. For example, the root depthsensor may be a probe with a measuring device (e.g., a root depth ruler)that a user inserts into the ground to extract a vegetation and soilsample, allowing the user to measure the length of the roots.Alternatively, the root depth sensor may capture certain root-relateddata and automatically input the root-related data into the system 100for the system 100 (e.g., the cloud) to analyze and estimate a rootdepth value. For example, the root depth sensor may include a camerathat captures an underground image that may be transmitted to the system100 for image processing to assess the root depth. The sensor 106 maytransmit the electrical signal to the network 114 and/or at least one ofthe controllers 102, 112 via hardwired or wireless methods (e.g., WiFi,radio signals, Bluetooth, etc.). The central controller 104 can receivedata from the sensor 106 to help generate an efficient watering plan.

The sensor 106 is typically positioned in, near, or adjacent to, anirrigation area watered by the watering system 100 and irrigationcontroller 102. The location and positioning of the sensor 106 may bevaried based on the size, location, vegetation variation, and/or weatherof the irrigation area.

The database(s) 116 may be an internal database of the system 100 or athird party database in communication with the system 100 over thenetwork 114. An internal database 116 may store data related to currentor historical watering schedules, vegetation characteristics, soilcharacteristics, zone characteristics, soil enhancement kits, and thelike. A third party database 116 may store data related to weather,vegetation characteristics (e.g., nutrient requirements based onvegetation type), soil characteristics, zone characteristics, and thelike. For example, a third party database 116 may include information onthe temperature of the soil in a particular geographical area.

Root Based Watering Plan

A watering plan is generated and implemented by the system utilizingvarying watering schedules that may be based on root depth of thevegetation in the selected area. For example, the watering plan may bevaried to provide incremental periods of stress to the roots toencourage them to grow, i.e., the system will reduce total wateringvolume and/or times to encourage root growth. Periods of stressencourage longer roots so that the plants can retain more water towithstand the periods of stress. Longer roots have a greater surfacearea and require greater amounts of water and/or a longer wateringduration. Because the longer roots will have access to a larger volumeof soil that stores more water for a given watering event, the frequencyof watering events can be reduced.

As water is delivered via sprinklers, it percolates through the soildefining a saturation zone or depth (e.g., the area or depth in the soilwhere the pores and fractures are saturated with water). The wateringplans generated by the system may generate watering times to deliversufficient water volumes to define saturation zones that engulf at leasta lower portion of the roots and extend just below the ends of thevegetation's roots. For example, the saturation zone may extend fromjust below the roots (e.g., from a lower boundary similar to lowerboundary 144 c of FIG. 2A, discussed in more detail below) along a lowerportion of the roots that is less than half the length of the roots. Asone example, the lower portion of the roots is less than one third thelength of the roots. As yet another example, the lower portion of theroots is less than one quarter the length of the roots. As one example,the system will select watering frequency and duration for wateringevents based on the estimated root depth, vegetation type, and soilconditions (e.g., soil porosity, water capacity, etc.). For example,FIG. 2A illustrates an example of varying watering depths for anexemplary plant. Each line 144 a-c represents the lower boundary of thesaturation zone. The water saturation depth 140 a-c varies based onnumerous factors, such as, for example, soil conditions (e.g., type,porosity, density, moisture level, and the like), water volume (e.g.,more water will saturate more of the soil creating a deeper lowerboundary), watering duration (e.g., longer watering time provides morewater that seeps deeper down into the pores creating a deeper lowerboundary), and the like.

As shown in FIG. 2A, with the same soil conditions, water percolationdepth varies based on water volume and watering duration for eachwatering event. For example, the water saturation depth 140 a may resultfrom a short watering duration and/or a low volume of water; the watersaturation depth 140 b may result from a long watering duration and/or ahigh volume of water; and the water saturation depth 140 c may resultfrom a watering duration and/or water volume that falls somewhere inbetween the respective watering duration or water volume of the otherwater saturation depths 140 a and 140 b.

As shown, different watering schedules for the same exemplary plant 142a-c result in different water saturation depths 140 a-c within the soil146. Each resulting lower boundary of saturation 144 a-c has a positionrelative to the plant roots 148 a-c. For example, the plant roots 148 aof the first plant 142 a extend below the lower boundary of saturation144 a. The watering schedule that generated this saturation zone is notideal, as the portion of the roots 148 a extending below the boundary144 a may not be able to access the deposited water. For the secondplant 142 b, the roots 148 b are engulfed within the saturation zone andthe lower boundary 144 b extends a substantial distance below the roots148 b. For example, as shown in FIG. 2A, the lower boundary 144 bextends to almost double the depth of the roots 148 b. The wateringschedule that generated this saturation zone is also not ideal, as theexcessive water below the roots 148 b cannot be utilized by the plant142 b and the vegetation is overwatered. For the third plant 142 c, thelower portion of the roots 148 c is engulfed by the saturation zone, butthe lower boundary 144 c extends only a small distance below the plantroots 148 c. For example, the depth of the roots 148 c is greater thanthree fourths of the water saturation depth 140 c. The watering schedulethat generated this saturation zone is preferable to the wateringschedules for the first and second plants 142 a,b, as the lower portionof the root is able to take up the surrounding water, withoutoverwatering. By generating a watering plan that incorporates periods ofstress with watering events that saturate the soil surrounding a lowerportion of the root and extending just below the roots (e.g., based onestimated root depth), the roots are encouraged to grow with minimalwasted water, resulting in healthier vegetation with water conserved.

FIG. 2B is a diagram illustrating an example of changes in root depthover time as a watering plan of the present disclosure is implemented.As shown, three phases of root growth are shown 126, 128, 130, each witha different root depth 132 a-c; however, there may be any number ofphases and root depths and those shown in FIG. 2B are meant asillustrative only. With reference to FIG. 2B, as the roots enter a newgrowth phase, the roots increase in length, and with this increasedlength, more water (e.g. longer watering duration and/or larger volume)is needed per watering event to sustain the vegetation. Each of thesephases may provide an updated schedule and feedback to the system, suchthat the system takes into account the variations in root depth whendetermining the watering schedules.

In one embodiment, the watering plan is dynamically adjusted as theroots enter new growth phases. This adjustment may be made daily,weekly, or according to another timeframe, depending on root growthrate. The root growth rate may be estimated based on the wateringschedule, growth rate of the vegetation (e.g., the change in vegetationheight over time), or may be input (e.g., through sensor data, userfeedback, or the like). In the example shown in FIG. 2B, Phase 1 126 maybe the initial root state before the watering plan is implemented, Phase2 128 may be the intermediate root state when the roots begin to grow asthe training watering plan is implemented, and Phase 3 130 may be thedesired root state achieved through the training watering plan. In thisexample, the root depth 132 a is the estimated root depth value and theroot depth 132 c is the optimal root depth value. The root depth may beestimated based on the height of the vegetation. For example, the rootdepth may be three times the height of the vegetation. In this example,therefore, the root depth 132 a can be estimated based on the height ofthe vegetation 134 a. The amount, duration, and frequency of waterprovided at each phase 1-3 126, 128, 130 varies proportionally with theroot depth 132 a-c. For example, at phase 1 126, the vegetation 134 ahas the shortest root depth 132 a and therefore requires less volume anda shorter duration of watering per watering event. Because the rootshave a shorter root depth 132 a, the roots will experience stress (e.g.,dry out) more rapidly and therefore require a greater frequency ofwatering events.

As the root depth increases from root depth 132 a at Phase 1 126 to rootdepth 132 b at Phase 2 128, the watering plan is dynamically adjusted.Since the vegetation 134 b has deeper roots 132 b, the volume of waterdelivered per watering event is increased, e.g., by increasing thewatering duration (length of time the sprinklers are on) and/or byactivating additional sprinklers covering the same area. Because theroots have a deeper root depth 132 b, the roots take longer to stress,allowing a longer time between watering events. As the root depthincreases from root depth 132 b at Phase 2 128 to the optimal root depth132 c at Phase 3 130, the watering plan is adjusted further. Since thevegetation 134 c has deeper roots 132 c, the volume of water applied perwatering event (e.g., by increasing the watering duration) is increasedwhile the frequency of watering events is decreased. In this example,since the optimal root depth 132 c has been reached at Phase 3 130, aminimum watering volume or threshold (e.g., a desired low overall amountof watering sufficient to maintain the vegetation without wasted water)is reached and the watering schedule is within the sustainable or greenzone.

FIG. 3 is a flow chart illustrating a method to establish a trainingwatering plan executed by the sprinkler controller. The method 150begins with operation 152 and the central controller or server 104receives historical data relating to the vegetation and watering zone(s)or property. For example, the central controller or server 104 canreceive historical data via network 114 from a user via a user device108 a, 108 n or from a stored database. It is also contemplated that theserver 104 may already have historical data stored if, for example, thewatering system 100 has previously been used in the same watering area,e.g., same backyard. Historical data may include historical wateringschedules, landscape usage (e.g., farming, irrigation, housing,recreation, etc.), weather variables (e.g., temperature, dew point,atmospheric pressure, etc.) and events (e.g., earthquakes, hurricanes,tornados, etc.), water supply and demand, and the like. Data relating tohistorical watering schedules may include such data as time of day ofwatering events, frequency of watering events, average amount of waterused per watering event, average duration of watering per wateringevent, average amount of water used per square foot of vegetation, orthe like.

With reference to FIG. 3, after operation 152, the method 150 proceedsto operation 154 and the central controller 104 receives data related tovegetation and soil specifications. For example, vegetation and soilspecifications may be input entirely or partially by a user via a userdevice 108 a, 108 n, input automatically by image recognition (e.g.,from video or image files from the sensor), or the like. For example,the user may capture images of the watering area and image detectionalgorithms may be executed to analyze the images for the vegetationtypes and coverage. As another example, a user may directly enter intothe system from their user device the types of vegetation coveringdifferent watering zones or areas covered by a select sprinklercontroller. It is contemplated missing data relating to vegetation andsoil specifications may be received from a public database 116 or adatabase 116 stored within the system 100. In other words, the user mayprovide input generally related to the vegetation type (e.g., KentuckyBlue Grass) and the system may pull specific vegetation and/or soilcharacteristics from one or more vegetation/soil databases. Vegetationspecifications may include vegetation type, cover, height, and density,among other data. Soil specifications may include soil type,composition, pack/density, porosity, water capacity, and moisture leveland depth, among other data, and may be based on the geographic locationof the sprinkler controller or user device. For example, the system maydetermine that the sprinkler controller is located at a particularlatitude and longitude, and then retrieve the soil characteristics froma database based on that latitude and longitude. As another example, theuser may input an address for the location of the sprinkler controllerand using the city and state or zip code, the soil characteristics maybe determined by referencing a soil characteristics database storingtypical soil characteristics for city, state or zip code. As yet anotherexample, the user may capture images of the soil, which may then beanalyzed and compared to known soil features to determine soilcharacteristics.

After operation 154, the method 150 proceeds to operation 156 and thecentral controller 104 estimates the current vegetation root depth 132.For example, the central controller 104 can use one or more of receivedhistorical data (e.g., watering schedules, landscape usage, weathervariables, frequency of watering events, and average amount of waterused, etc.), vegetation data (e.g., type, height, cover, density, etc.),and soil data (e.g., type, composition, density, porosity, watercapacity, moisture level and depth, etc.), to predict the root growthspeed, determine the change in root depth overtime, and estimate thecurrent root depth. As one example, the root depth can be estimatedbased on the height of the grass. In one embodiment, the system mayestimate that the root depth is around 3 times the height of the grass.Utilizing a user input value of grass height or an image detection ofthe grass, the height of the grass is estimated by the processingelement of the system and then with the grass height the estimate rootvalue is determined, e.g., between 2-4 times the height of the grass.Other relationships may be used as well to determine the estimated rootdepth.

After operation 156, the method 150 proceeds to operation 158. Inoperation 158, the central controller 104 uses the estimated root depth132 to determine a minimum watering threshold for a predetermined timeperiod or number of watering events. In other words, the smallest watervolume that the vegetation can receive over a selected period of time isdetermined. The minimum overall watering threshold may be selected basedon the smallest amount of water necessary to maintain the vegetation ata healthy, sustainable level, where the amount may be determined basedon per day or per week basis, e.g., X gallons every three days. Ahealthy, sustainable level may be based on 30 to 50 percent of thevegetation showing signs of wilt or thirst. Thirst is typicallydetermined by analyzing folding leaf blades, blue-gray color, andimpressions, such as footprints, remaining in the vegetation. A healthy,sustainable level may also be based on chlorophyll content. For example,satellite imagery of chlorophyll concentration may be measured to assessplant health. Because the length of the root correlates to the overallamount of water needed for the vegetation to survive (specifically alonger root requires less overall water since it can sustain longerperiods of stress without watering), this minimum overall wateringthreshold can be calculated based on the estimated root depth 132.

As one example, the system may use the following equation:

Min. overall watering threshold (in.)=optimal root depth (in.)×min.overall water needed (in.)/in. of root

In the above equation, the minimum watering threshold (in inches ofwater required per day/week/month/year/etc.) can be correlated to thesmallest total amount of water needed per inch of root multiplied by theoptimal root depth, where the optimal root depth is a calculatedvariable based on the change in the estimated root depth overtime. At ahigh level, by determining the estimated root depth, and determining thetotal water needed per inch of root, the system can estimate the minimumwatering threshold and predict the total water needed for the currentvegetation for the selected time period, e.g., every day, every threedays, every 7 days, or the like. The time period may be selected asintervals used to select the watering schedule or may be separatetherefrom.

With reference again to FIG. 3, after operation 158, the method 150 mayproceed to operation 160 and the central controller 104 may receive userpreference data from a user device 108 a, 108 n via the network 114. Forexample, a user may prefer lusher vegetation than that generated bywatering the vegetation at its minimum watering threshold within thegreen zone. The user may desire to input this preference into the systemduring the training phase. In an alternative example, the user may firstobserve the appearance of the vegetation after the green zone wateringplan is implemented, and then decide to adjust the green zone wateringplan according to his or her preference. In one example, the green zonewatering plan may be considered an eco mode, while the user-modifiedgreen zone watering plan may be considered a lush mode. In this example,a user can select either eco mode or lush mode on the user device 108 a,108 n based on user preference. Other modes are contemplated to factorin different user preferences.

After operation 160, the method 150 proceeds to operation 162 and thecentral controller 104 determines the training watering plan. If no userpreference data was received at operation 160, the central controller104 generates a training watering plan at operation 162. The training orselect watering plan may aim to water the vegetation within the greenzone or within some other predetermined watering range. Based on theestimated root depth of operation 156, and the minimum wateringthreshold or minimum watering volume determined in operation 158, thecentral controller 104 builds a training watering plan to increase theroot depth over time to allow reduced overall watering for thevegetation. The training watering plan may vary an amount of waterdistributed to the vegetation based on days of the week, times of day,or increments of time. For example, the training watering plan may allota certain amount of water for 10 minutes at 6 am and a different amountof water for 10 minutes at 6 pm on Monday, Wednesday, and Thursday. Asthe root depth increases, and the roots become more drought resistant,the roots can better withstand water schedule adjustments, such thatwatering can take place at the most effective and efficient time of day.

If user preference data was received at operation 160, the userpreference data is factored into the training watering plan to generatea user-modified training watering plan at operation 162 where the endwatering amount may be increased above a minimum threshold. In otherwords, the modified watering plan may distribute a total watering volumenear the green zone, but that is larger based on the user's preferencesfor the vegetation.

After operation 162, the method 150 proceeds to operation 164. Inoperation 164, the central controller 104 may store the trainingwatering plan and transmit the plan via the network 114 to at least oneof the controllers 102, 112 to implement the watering plan. It is alsocontemplated that the central controller 104 can store data collectedand data generated at each operation of method 150 within one or morememory components 258 of a computing device 250 (See FIG. 12).Alternatively, in some embodiments, the controllers 102, 112 maythemselves execute the various operations to generate the trainingwatering plan and in these instances may locally save the plan forexecution.

FIG. 4 is a flow chart illustrating a method for dynamically modifying awatering plan. After a watering plan is determined, such as in operation162 in the method 150 of FIG. 3, the method 200 of FIG. 4 may maintainor modify the watering plan. The watering plan may be a trainingwatering plan or the ultimate green zone watering plan. As discussedpreviously, the training watering plan delivers via the sprinklersselected volumes of water to train the roots to grow longer, allowingthe vegetation to require less water. The green zone watering plan isthe on-going or sustainable watering plan, i.e., the watering scheduleapplied when the roots have achieved an optimal or desired depth and maybe selected to maintain the optimal or desired root depth. For example,when the roots have reached the optimal or desired depth, the wateringplan may be selected so as to no longer aim to encourage root growth,i.e., the watering events may not be selected to reduce the water volumeto encourage root growth; instead, the green zone watering plan may takeinto account other variables, such as, for example, local weatherforecasts (e.g., precipitation forecasts, temperature forecasts, etc.),environmental events (e.g., hurricanes, rain storms, etc.), and thelike, to ensure the overall watering maintains the roots at the optimalor desired depth (e.g., to ensure that the overall watering remains atthe minimal overall watering threshold).

The method 200 begins with operation 202 and the watering plan istransmitted from the central controller 104 to at least one of thecontrollers 102, 112. The controllers 102, 112 may then implement thewatering plan, e.g., selectively activate sprinkler heads to water oneor more zones based on the watering times provided in the watering plan.The central controller 104 may control the time, day and frequency ofsuch signals in accordance with the watering plan.

After operation 202, the method 200 proceeds to operation 204 and thecentral controller 104 receives feedback data to determine whether tocontinue implementing the watering plan or to modify the watering plan.Feedback data can be in the form of user preferences, programmedautomatic feedback, new root depth measurement data, computer learning,or the like. In one example, feedback data can be provided by a userthrough a user device 108 a, 108 n. For example, if the user wants alusher or greener lawn, the user can modify the watering plan through auser device, e.g., the user can select a “lush mode” in an applicationon the user's smart phone to modify a green zone watering plan toincrease watering volumes, which will likely increase the greenappearance of the lawn. In yet another example, specifications may bepre-programmed within the system 100, and one or more sensors can detectwhether such specifications have been met and send feedback data to thecentral controller 104. The central controller 104 can then maintain ormodify the watering plan accordingly in order to achieve thepre-programmed specifications. As an example, a user can program adesired vegetation coloration into the system 100. A camera can captureimages of the vegetation, and the images can be analyzed to determinecurrent vegetation coloration and send this data to the centralcontroller 104, which can then process this information to determine ifthe current coloration matches the programmed coloration. If the currentcoloration does not match the desired programmed coloration, the centralcontroller 104 will modify the watering plan accordingly to achieve thedesired coloration.

Depending upon whether feedback data is received at operation 204, themethod 200 will proceed either to operation 208 or to operation 206. Ifno feedback data is received, method 200 will proceed to operation 208and continue to the next phase of the watering plan of operation 202with no modifications. If the watering plan of operation 202 is thegreen zone watering plan, the central controller 104 will continue toimplement a watering plan that aims to achieve a watering range withinthe green zone. If the watering plan of operation 202 is a predeterminedwatering plan, the central controller 104 will continue to implement awatering plan that aims to achieve a watering range near the green zonethat incorporates any previously received feedback data, such as, forexample, in operation 160 of FIG. 3.

If feedback data is received at operation 204, the method 200 willproceed to operation 206. In operation 206, the central controller 104will determine whether the feedback data of operation 204 requires achange or modification of the watering plan. Depending upon whether thefeedback data requires that the watering plan be changed, the method 200will proceed either to operation 208 or to operation 210. As an example,feedback data will not require a change in the watering plan where thefeedback data indicates that the current watering plan aligns with userpreferences. For example, if a camera is used to detect vegetationcoloration and the feedback data indicates that the current colorationmatches the desired coloration, then the watering plan does not need tobe changed and the method 200 would proceed to operation 208. If,however, the feedback data indicates that the current coloration isdifferent from the desired coloration, then the watering plan would needto be modified to achieve the desired coloration and the method 200would proceed to operation 210. If the central controller 104 determinesa change in the watering plan is not necessary based upon the feedbackdata received, then the method 200 will proceed to operation 208 andcontinue to the next phase of the watering plan with no changes. If,however, the central controller 104 determines that a change isnecessary based upon the feedback data received, then the method 200will proceed to operation 210 and the central controller 104 will modifythe watering plan.

In operation 210, the central controller 104 will modify the wateringplan based upon the feedback data received. The modification can beeither a slight or a major variation from the watering plan. Themodification can also be either an increase or a decrease in water use.For example, if a green zone watering plan was implemented in operation202, then a likely modification will be to increase the volume of waterused, the duration of watering events, or the frequency of watering. Asan example, if a user desires a lusher or greener appearance than thatprovided by implementing the green zone watering plan, the user mayprovide user feedback from a user device 108 a, 108 n, and the centralcontroller 104 can then modify the watering plan to provide for morewater allowance to cultivate lusher vegetation.

As an alternative example, if a predetermined watering plan wasimplemented in operation 202, then the central controller 104 may modifythe watering plan to increase total water used or it may modify thewatering plan to implement a green zone watering plan, using lessoverall water and aiming to achieve a watering level within the greenzone. For example, if in method 150 of FIG. 3, the central controller104 received user preference data in operation 160, the predeterminedwatering plan may already aim to achieve a total watering range abovethe green zone. The user may provide additional feedback at operation204 of method 200 to the central controller 104 indicating a desire toreduce water consumption. The central controller 104 could use thisfeedback to modify the watering plan to use less overall water inoperation 210 of method 200. If, as another example, the user providesadditional feedback to the central controller 104 at operation 204indicating a desire to have even lusher vegetation (a desire for achange at operation 206), then the central controller 104 would use thisfeedback to modify the watering plan to use even more overall water inoperation 210 of method 200.

After operation 210, the method 200 proceeds to operation 212. Inoperation 212, the central controller 104 stores the updated wateringplan as the current watering plan. The central controller 104 may storethis data within internal or external memory components.

FIG. 5 is a flow chart illustrating an example of utilizing landscape oryard characteristic inputs to generate a watering plan. The method 220begins with operation 222 and the central controller 104 estimates orreceives the current root depth value. For example, various data may beanalyzed to estimate a root depth value, including, among other things,at least one of historical watering data, vegetation specifications, andsoil specifications. As another example, root depth may be measureddirectly by a user or by a sensor and input into the system 100. Forexample, a user may measure root depth for a sample of the vegetationand input the measurement into the system 100 via a user device 108 a,108 n. In this example, the system 100 may extrapolate this measurementdata to estimate the root depth for the entire vegetation plot (e.g.,based on vegetation type, cover, soil conditions, etc.).

After operation 222, the method 220 proceeds to operation 224 and thecentral controller 104 determines the optimal root depth value. Theoptimal root depth value may be the depth at which the root is droughtresistant, such that the root can sustain the vegetation at its minimumwatering threshold, or another predetermined root depth. The optimalroot depth value is determined based upon different variables, such as,among other things, the current root depth, the vegetation and soilspecifications, and environmental factors, and may include userpreferences.

After operation 224, the method 220 proceeds to operation 226 and thecentral controller 104 determines the green zone watering plan. Thegreen zone watering plan is correlated to the optimal root depth. Thegreen zone watering plan may include a reduced watering event frequencyand/or reduction of watering volume levels, while maintaining thevegetation at a minimum watering threshold based on the optimal rootdepth. The green zone watering plan can be achieved by reducing thefrequency of watering events, increasing the time between wateringevents, and increasing the watering volume/duration per watering eventover time as the current root depth grows to achieve the optimal rootdepth. The central controller 104 generates a watering planincorporating changes in watering volume and frequency over time as theroots grow.

After operation 226, the method 220 proceeds to operation 228 and thecentral controller 104 receives data on local water supply and demandforecasts. For example, the central controller 104 may receive watersupply and demand forecasts from a weather service, third party, or thelike. Water supply and demand forecasts can be received by the day or bythe hour to assist in determining an acceptable moisture level range.

After operation 228, the method 220 proceeds to operation 230 and thecentral controller 104 generates a watering plan. Data collected inoperations 222, 224, 226, and 228 are factored into the watering plan.For example, the initial overall watering volume can be dynamicallyadjusted over time to achieve the green zone watering plan (e.g., theminimum watering threshold) for the optimal root depth value. Thisadjustment can take place hourly, daily, or weekly. The water supply anddemand forecast may be taken into account to achieve a watering planallowing efficient watering while still protecting the local waterresources. As an example, where water supply is high and demand is low,the total amount of water applied to the vegetation can be increased. Asanother example, where water supply is low and demand is high, the totalamount of water applied to the vegetation may be reduced. For example,if a user enters a user preference for lush mode as opposed to eco mode,which allows for watering above the green zone, the total amount ofwater applied in excess of the green zone may be higher or lowerdepending on local water supply and demand forecasts.

The central controller 104 may continuously evaluate the root depth inthe manner described previously and determine whether the wateringtimes, frequency, and/or days should be varied. If the centralcontroller 104 determines that the watering plan should be updated, thecentral controller 104 may store the updated watering plan and/ortransmit the updated watering plan via the network 114 to at least oneof the controllers 102, 112.

FIG. 6 is a flow chart illustrating a method to establish a wateringplan. The method 270 begins with operation 272 and vegetation and soilspecifications are received. Vegetation specifications may includevegetation type, cover, height, and density, among other data. Soilspecifications may include soil type, composition, pack/density,porosity, water capacity, and moisture level and depth, among otherdata. Vegetation and soil specifications may be input, for example,entirely or partially by a user via a user device 108 a, 108 n, inputautomatically by image recognition (e.g., from video or image files fromthe sensor), or the like. It is contemplated missing data relating tovegetation and soil specifications may be received from a publicdatabase 116 or a database 116 stored within the system 100.

After operation 272, the method 270 proceeds to operation 274 andweather and/or environmental data is received. Weather data may include,for example, precipitation, humidity, atmospheric pressure, temperature,weather events (e.g., hurricane), and the like. Weather data may includehistorical weather patterns and/or current weather forecasts.Environmental data may include, for example, sun exposure, landscapeuse, landscape age, historical watering patterns (e.g., historicalduration, frequency, and volume of watering events), and the like. Theweather and/or environmental data may be input into the system 100 by auser, by one or more sensors (e.g., a thermometer, barometer, etc.), orvia one or more databases. For example, the weather data may be receivedfrom a third party database that monitors and stores weather data. Asanother example, the historical watering patterns may be received froman internal database associated with the system 100.

After operation 274, the method 270 proceeds to operation 276 and thecurrent root depth value is determined (e.g., by actual measurementstaken from a soil sample) or estimated. The root depth value may beestimated based on one or more of the vegetation data, soil data,weather data, and environmental data collected at operations 272 and274. As one example, the root depth can be estimated based on the heightof the grass. For example, the root depth may be about 3 times thelength of the grass.

After operation 276, the method 270 proceeds to operation 278 and thewater depletion threshold is determined. The water depletion thresholdis the maximum amount of allowed water depletion from the soil to stressthe roots but enable survival of the vegetation. The water depletionthreshold corresponds to an amount of stress allotted to the vegetation(e.g., a greater water depletion threshold provides more stress to thevegetation). The amount of stress allotted to the vegetation may dependon root depth, such that the water depletion threshold may be determinedbased on the current root depth estimated at operation 276. For example,the water depletion threshold (e.g., allotted stress) may be greater forlonger roots. For example, the water depletion threshold may be 50% ormore, 60% or more, 70% or more, 80% or more, 85% or more, 90% or more,95% or more allotted water depletion. The water depletion threshold mayalso depend on the yield curve of the vegetation. In these instances,the yield curve may also be factored into the determination of the waterdepletion threshold. For example, depending on the yield curve and thetime of year for the vegetation, the water depletion can be reduced asthe vegetation may be expected to be in a slow growth period based onthe yield curve or vice versa.

After operation 278, the method 270 proceeds to operation 280 and thewater depletion rate is estimated. The water depletion rate is theamount of water depleted from the soil over time. The water depletionrate may depend on several factors, such as, for example, soil watercontent, soil porosity/density, atmospheric pressure and humidity,temperature, evapotranspiration (ET), and the like. For example, thewater depletion rate may be greater with less soil water content,greater soil porosity, lower pressure and humidity, and a highertemperature. The water depletion rate may vary based on the time ofyear. For example, warmer and drier months/seasons (e.g., summer) mayhave a faster water depletion rate. As one example, ET may be calculatedusing the Penman-Monteith equation and then used to determine an amountof water depletion.

After operation 280, the method 270 proceeds to operation 282 and awatering plan is generated. The watering plan may be generated toincrease root depth based on the data collected at operations 272 and274, the estimated or actual current root depth, the determined waterdepletion threshold, and the estimated water depletion rate. Forexample, the water volume allotted per watering event may correspond tothe root depth (e.g., a larger volume of water may be used to water thelarger volume of soil that surrounds deeper roots). The volume of waterallotted may depend on soil characteristics such as, for example, soilporosity, water capacity, water content, and the like, and weather andenvironmental factors that affect the water content of the soil. Asanother example, the training time (e.g., non-watering time betweenwatering events), which corresponds to a period of stress, may bedetermined based on the water depletion threshold and the estimatedwater depletion rate. For example, the training period may be longerwith a greater water depletion threshold. For example, a training periodmay last until the water in the soil has depleted to a certain level(e.g., depleted by 80% or more). The training period may also be longerif the water depletion rate is low (e.g., since it takes longer for thewater in the soil to deplete to the threshold depletion level). Becausethe water depletion rate may vary depending on the time of year (e.g.,cooler and damper months may have a low depletion rate), the trainingperiod may also vary depending on the time of year. With longer trainingperiods, the frequency of watering events may be decreased.

After operation 282, the method 270 proceeds to operation 284 and thesystem 100 may store the watering plan and transmit the watering planvia the network 114 to at least one of the controllers 102, 112 toimplement the watering plan. It is also contemplated that the system 100can store data collected and data generated at each operation of method270 within one or more memory components 258 of a computing device 250(See FIG. 12). Alternatively, in some embodiments, the controllers 102,112 may themselves execute the various operations to generate thewatering plan and in these instances may locally save the plan forexecution.

Soil Enhancement Kit

FIG. 7 is a flow chart illustrating a method to generate and apply asoil enhancement kit to improve vegetation health. The method 300 beginswith operation 302 and vegetation and zone data are received. Vegetationdata may include vegetation type (e.g., species), location, root depth,crop coefficient, yield curve, density, cover, height, color,chlorophyll content, and the like. Vegetation data may be input by auser or determined by the system 100. For example, a sensor 106 maymonitor data related to vegetation characteristics and input the datainto the system 100 for processing. As one example, the sensor 106 maybe an optical sensor and may capture an image of the vegetation. Asanother example, the sensor 106 may be a soil sensor that detects soilmoisture levels. The system 100 may be able to assess differences incolor of the vegetation, the percent cover of the vegetation, thespecies of the vegetation, the height of the vegetation, yield curves,the root depth, and the like.

Zone data may include landscape or area location (e.g., latitude andlongitude), use, age (e.g., less than one year old, 2 years old, 5 yearsold, 10 years old, 20 years old, etc.), size, maintenance, wateringdata, and the like. For example, landscape use data may include type oflandscape use (e.g., farm land, residential, communal, etc.), howfrequently the landscape is used (e.g., rarely, often, daily, weekly,etc.), the intensity of use (e.g., heavy vs. light), and the like. Forexample, the landscape may be a front yard and may rarely be used. Asanother example, the landscape may be a back yard and may be used oftenfor children to play and animals to roam. Landscape size data mayinclude acreage, coverage area, and the like. In some examples, thelandscape size may be an average zone size based on zone data ofsurrounding areas. In some examples, the landscape size may be anarbitrary number (e.g., 500 sq. ft.) or may be determined from adatabase storing landscape size data. Landscape maintenance data mayinclude data related type of maintenance, maintenance timing, frequencyof maintenance, and the like. Maintenance may include mowing, mulching,bagging, applying fertilizer, planting, weeding, and the like. Wateringdata may include current and historical watering patterns (e.g.,watering frequency and duration). Zone data may also include weatherdata (e.g., precipitation and/or wind patterns).

The zone data may be received from a user, from the system 100, or froma third party database. As one example, a user may input one or more ofthe zone data into the system 100. For example, the user may input thefrequency and type of activity taking place in the yard (e.g., kids playevery afternoon in the backyard, a dog is allowed to roam in the fencedin backyard, no one uses the front yard, etc.). As another example, asensor may collect one or more of the zone data and input the collecteddata into the system 100. For example, a sensor (e.g., motion sensor,camera, etc.) may monitor landscape use/maintenance and input thelandscape use/maintenance data to the system 100. As another example,the landscape size/area may be received from a user, a third partydatabase, or determined by the system. For example, an image of the yardmay be captured (e.g., by a camera) and uploaded by a user, and thesystem may estimate the area of the yard based on the image. Forexample, the system may trace a polygon around the yard and determinethe measurement of the enclosed area. As another example, the landscapemay be divided into various zones, each with a mapped area, and thesystem 100 may be able to determine the total landscape area by addingthe zone mappings together. As yet another example, the landscape sizemay be retrieved from a third party database (e.g., property records)and determined, for example, based on square footage of the house andlot size. As another example, the weather data may be received from athird party database that monitors and stores weather data.Alternatively or in addition, the system 100 may determine weather databased on data collected from sensors monitoring weather (e.g., athermometer, barometer, etc.).

After operation 302, the method 300 proceeds to operation 304 and soildata is received. Soil data may include soil type, density, porosity,moisture level, temperature, and the like. The soil data may be receivedfrom a user, from the system 100, or from a third party database. Forexample, a third party database may provide soil temperature data basedon the location of the landscape area (e.g., the latitude andlongitude). As another example, a sensor 106 (e.g., thermometer, camera,etc.) may determine certain soil characteristics (e.g., temperature,porosity, etc.) and input them into the system 100. As yet anotherexample, a user may take a soil sample (e.g., via a soil probe or plug)and provide the system 100 with various measurements related to the soilcharacteristics. As one example, the user may upload an image of thesoil sample and the system may use image recognition techniques toanalyze the soil sample. For example, an image recognition technique mayuse feature extraction based on spatial features, edge detection,boundary extraction, contour following, shape features, textures, andthe like. With such techniques, various characteristics of the soil maybe determined, such as, for example, grain size and distribution, voids(e.g., pores), nutrient content, pH level, water content, and the like.

After operation 304, the method 300 proceeds to operation 306 and arecipe is generated for a soil enhancement kit. The soil enhancement kitincludes one or more components or consumables that, when applied to thesoil, improve overall landscape health (e.g., improve soil conditionsand vegetation health). For example, certain components may be includedthat help to open the soil in order for the soil to better retain water,encouraging root growth. The soil enhancement kit may include one ormore of the following components, in any combination: macronutrients(e.g., nitrogen, phosphorus, potassium, etc.), micronutrients (boron,copper, iron, manganese, molybdenum, zinc, nickel, chloride, etc.),fertilizer, soil conditioner, weed control, wetting agent, biostimulant, bacteria, fungi, organic matter, plant hormones, and thelike. In some examples, the kit may include synthetic fertilizer. Inother examples, the kit may include organic matter and one or more fungiand/or bacteria. For example, a particular combination of fungi and/orbacteria may consume particular organic matter to make certain nutrientsavailable to the vegetation. In several embodiments, one or morecomponents in the soil enhancement kit are liquid. For example, theentire soil enhancement kit may be liquid. However, it is alsocontemplated that one or more components may be solid (e.g., a powder).The amount and type(s) of the one or more components included in the kitmay be varied based on data related to the landscape. For example, thelandscape data may include information on vegetation characteristics,current vegetation health (including, for example, current vegetationand soil conditions) and target vegetation health, local weather,seasonal changes, climate, historical watering patterns, currentwatering plan, and the like.

The vegetation characteristics may be used to determine the componentsof the soil enhancement kit and the component amount. For example,nutritional demand may vary based on the vegetation species. Vegetationspecies nutritional demand may be determined, for example, from adatabase. The required nutrients may be included with the soilenhancement kit or one or more microbes may be included that helpmobilize the required nutrients already within the soil. For example,different bacterial or fungal species may mobilize different nutrients.The bacterial and/or fungal species included with the kit may beselected based on the nutrients required by the vegetation species. Asanother example, the amount of nutrient or bacterial and/or fungalspecies included may vary based on vegetation cover and landscape area.

The current landscape health can be determined based on at least one ofthe data collected at operations 302 and 304. For example, greaterlandscape use may disrupt the soil and hinder vegetation growth,resulting in less durable soil and a more unhealthy land. As anotherexample, the age of the soil and vegetation may reveal generalinformation about activity and stabilization of the soil. For example,landscape with younger characteristics, e.g., with recent landscaping(e.g., less than a year old), may have new sod that does not have muchdepth, nutrition, or microbial activity, while landscape with oldercharacteristics (e.g., 10 years old) may have better depth, nutrition,and well-established microbial activity and may therefore be healthier.Different maintenance techniques may also have varied impact on thesoil. For example, whether the vegetation is bagged or mulched may varythe nutrients in the soil as bagging may remove soil nutrients, whilemulching may add nutrients into the soil. As another example, currentand historical watering patterns may be indicative of vegetation healthand soil conditions. For example, historical overwatering may indicateshorter root depth and high soil moisture levels. As yet anotherexample, excessive precipitation may result in runoff, which can affectthe ability of the soil to retain nutrients.

Based on the determination of the current landscape health (e.g.,estimated durability, condition of nutrients, microbes, and other soilconditions), the components of the kit can be determined to achieve thedesired or target landscape health. In some embodiments, the system maydetermine soil deficiencies (e.g., deficiencies in composition (e.g.,nutrients, microbes, etc.), in moisture, in porosity, and the like), andprovide the deficient soil components, or remedies for the deficientcomponents, with the kit. For example, if nitrogen is deficient (e.g.,nitrogen has been removed from the soil through bagging, runoff, or thelike), then nitrogen and/or particular microbes may be included in thekit. On the other hand, if nitrogen has been added to the soil throughmulching, then the system may determine the soil has sufficient nitrogenand omit this component from the kit. In some cases, nitrogen may alsobe reduced in the kit based on the age of the soil. For example, moremature soil likely has more stable, self-contained microbes, andtherefore requires less nitrogen. As another example, if the ground israrely weeded, then a weed controller may be included in the kit. Theamount of each component in the kit will depend on, among other things,the size of the landscape and the vegetation coverage and type.

After operation 306, the method 300 proceeds to operation 308 and theapplication time frame and frequency for the kit is determined. Theremay be a specific window of time that is optimal to apply the kit. Forexample, timing of application may depend on soil conditions. As oneexample, the ground temperature may need to reach a certain thresholdtemperature for the kit to be applied. As discussed above, the groundtemperature may be determined by a sensor 106 or by a third partydatabase (e.g., based on weather at the location or typical groundtemperatures based on time of year for that location). Once the groundreaches the threshold temperature, the kit can be applied. The window oftime for kit application may be defined by the ground temperature. Forexample, the kit may be applied when the ground temperature is within acertain range, and when the ground temperature falls outside the range,kit application is no longer recommended. In this manner, thetemperature may create a start and stop time for kit application. Thecomponents of the kit may be applied at once or at different times(e.g., staggering application). For example, two or more kit componentsmay thrive at different soil temperatures, such that the components areapplied at different times based on the ground temperature. The kit mayinclude multiple doses (e.g., in separate individual packages) that canbe applied at different times. For example, the doses may be applieddaily, weekly, or monthly, depending upon the specific needs of theland. The application timing and frequency may also depend on the typeof vegetation (e.g., the species). For example, different vegetationspecies may require different amounts of particular nutrients, such thatsome vegetation types may require a greater frequency of nutrientapplication than other vegetation types.

After operation 308, the method 300 proceeds to operation 310 and thekit and kit instructions are transmitted to a user. The kit instructionsmay include information on when and how to apply the kit. For example,the instructions may include information on the time window, frequency,and/or duration of application, the amount to apply, and the location toapply the kit in (e.g., if the landscape has various types ofvegetation). For example, the frequency of application may depend onvegetation type/species. For example, the kit may need to be appliedevery week, every two weeks, every other week, etc. based on the type ofvegetation.

The kit may be transmitted to the user as an individualized packet. Thekit may include only a single application (e.g., single dose) ormultiple applications/doses. The amount of doses provided in the kit mayvary based on the lifespan of the kit contents (e.g., based on the typeof component, the potency of the components over time, etc.). Forexample, only a single dose may be provided per kit where the kitcontents have a short lifespan (e.g., quickly lose potency). Forexample, certain microbes may only last within the kit a certain amountof time. The kit may include an amount sufficient for the entirelandscape area. In some embodiments, one or more components of the kitmay be divided into separate packets for separate application. Forexample, one component may be applied and, after an activation periodfor the first component, as discussed in more detail below, one or moreother components may be applied. For example, bacteria may be appliedfirst and, after a colonization period, a fungus may be applied.

In some embodiments, the kit may be sent to the user when the kit needsto be applied. For example, when the system 100 determines the groundtemperature has reached the applicable temperature range (e.g., thetemperature appropriate for kit application), the system 100 maytransmit the kit to the user, e.g., the system may schedule the kit tobe delivered (e.g., by mail or other delivery) to the user's mailingaddress or other location. In these embodiments, the system 100 may beselected to arrange delivery of the kit to the user a week, a day, or afew hours before the kit needs to be applied. Alternatively, the kit maybe sent to the user when generated, and the instructions may indicatewhen the kit needs to be applied. In some embodiments, the kit may besent at set intervals of time to the user. For example, depending on thenumber of doses provided in each kit, the lifespan of each kit, thelandscape area, and the individual landscape health needs, the amount oftime between each kit shipment may be determined. For example, a kit maybe shipped to a user once a week, every two weeks, once a month, everytwo months, once a year, etc.

After operation 310, the method 300 proceeds to operation 312 andfeedback on vegetation health is received. Generally, the vegetationhealth will improve over time as the soil enhancement kit is applied.For example, the soil enhancement kit may increase the available waterin the soil, the crop coefficient, and the yield curve. The timeframefor vegetation health improvement may correspond to an activationperiod, e.g., a time over which the one or more components of the soilenhancement kit or the one or more byproducts of the soil enhancementkit (e.g., mobilized nutrients) are introduced and have an impact on thesurrounding environment). For example, the activation period may be aninoculation period (e.g., time for association with the plant and/orabsorption by the plant), an incubation period (e.g., time for microbeincubation/colonization), a mobilization period (e.g., period over whichimmobilized nutrients become mobilized), and the like. As one example,where the soil enhancement kit includes one or more fungi, the fungusmay extend the reach of the roots into the surrounding soil as the rootsare colonized by the fungus over an inoculation or colonization period.For example, mycorrhizal fungi colonize plant roots, helping solubilizephosphorus and bring soil nutrients and water to the plant. Mycorrhizalfungi enhance nutrient update of the roots by increasing the surfaceabsorbing area of roots and releasing chemicals into the soil thatdissolve complex soil nutrients (e.g., phosphorus, iron, etc.). Forexample, one group of mycorrhizae, endomycorrhizae, grow within rootcells and are commonly associated with grasses, row crops, vegetables,and shrubs. In this manner, vegetation health may be predicted toimprove over a colonization period.

Feedback received at operation 312 may be received from a user or fromthe system 100. For example, a user may desire a lusher, greener lawnand send a request to the system 100 for an additional kit or additionalkit components. As another example, the system 100 may monitor thehealth of the landscape over time and provide feedback. As one example,one or more cameras may be placed on the landscape and may capture imagedata. The image data may be sent to the system 100 for image processing.The system 100 may process the image to determine vegetation color,chlorophyll content, height, root depth, soil density/porosity, and thelike. As another example, other sensors 106 may be used to monitor othercharacteristics, such as soil moisture levels, nutrient levels,microbial activity, and the like. The system 100 may use the collecteddata to determine overall landscape health. For example, greener andhigher vegetation with greater chlorophyll content and a longer rootdepth may indicate overall healthy landscape, while browner, shortervegetation with less chlorophyll content and shorter root depth mayindicate overall unhealthy landscape.

In some embodiments, the feedback received at operation 312 may includedata related to the implementation of the soil enhancement kit (e.g.,whether the kit was applied, when the kit was applied, whether kitapplication followed the instructions, etc.). This feedback may beprovided by a user or by the system 100 (e.g., a sensor may detectapplication data (e.g., occurrence, rate of application, amount ofapplication) and send this data to the system 100). The system 100 maydetermine landscape health status based on whether, how, and when thekit was applied.

After operation 312, the method 300 proceeds to operation 314 and thesystem 100 determines whether to adjust the kit composition. Forexample, based on the feedback on current overall landscape health ordesired landscape health, the kit may need to be adjusted. For example,if overall landscape health is improving, then the soil may requirefewer additives/supplements. As another example, if the user desires alusher lawn than provided by the current kit generated at operation 306,then the kit may be adjusted to include more additives/supplements. Ifthe kit composition needs to be adjusted, then the method 300 proceedsto operation 306 and a new recipe is generated for the soil enhancementkit. If the kit composition does not need to be adjusted (e.g., thecurrent landscape health has reached the desired landscape health), thenthe method 300 ends.

After operation 312, the method 300 also proceeds to operation 316 andthe system 100 determines whether to adjust the dynamic watering plan.As previously discussed, the dynamic watering plan may depend on severalfactors related to vegetation health and soil conditions. For example,the factors may include root depth, chlorophyll content, cropcoefficient, yield curve, soil water capacity, soil water content, soilwater depletion rate, soil water depletion threshold, soil temperature,and the like. An applied product (e.g., a soil enhancement kit) and anactivation period may impact these factors, altering vegetation healthand soil conditions and resulting in a need to adjust the watering planaccordingly. For example, vegetation health may improve over or after anactivation period after application of an applied product (and, in somecases, after an initial watering event), and the watering plan may beadjusted as vegetation health improves. Therefore, the watering plan maybe adjusted based on the activation period. As one example, applicationof the soil enhancement kit may increase soil porosity enabling roots togrow deeper. For example, application of a soil conditioner from the kitwill help to aerate the soil, creating space beneath the soil for theroots to grow. Longer roots take up a greater volume of soil, requiringan increased volume of water to fully submerge the lower portion of theroots to allow the roots to grow even deeper. Further, longer roots cansurvive with greater water depletion, such that the allowed waterdepletion threshold can be increased, as discussed in more detail below.In this example, the increased root length results in a need to adjustthe dynamic watering plan.

As another example, the soil enhancement kit may alter the soil watercapacity, water content, water depletion rate, and the like. Forexample, the kit may increase the soil water capacity and water content,which may result in a decreased water depletion rate (e.g., a longertime is required for the increased water content in the soil todeplete). The watering plan may include training periods (e.g.,non-watering periods) that are based on the water depletion rate and awater depletion threshold. The water depletion threshold is the amountof water that is allowed to deplete from the soil (e.g., prior towatering). The water depletion threshold corresponds to an amount ofstress allotted to the vegetation (e.g., a greater water depletionthreshold provides more stress to the vegetation). The amount of stressallotted to the vegetation may depend on root length or depth, such thatthe water depletion threshold may be determined based on root length ordepth. For example, the water depletion threshold (e.g., allottedstress) may be greater for longer roots. The training period, whichcorresponds to a period of stress, may be increased for longer rootsthat have a greater water depletion threshold. For example, a trainingperiod may last until the water in the soil has depleted to a certainlevel (e.g., depleted by 80% or more). If the water depletion ratedecreases, for example, then the training period may last longer (e.g.,since it takes longer for the water in the soil to deplete to thethreshold depletion level). In this example, the dynamic watering planmay need adjustment with an increase in soil water capacity and watercontent and a decrease in the water depletion rate.

If the system 100 determines at operation 316 that the watering plandoes not need adjustment, the method 300 ends. If the system 100determines at operation 316 that the watering plan needs adjustment, themethod 300 proceeds to operation 318 and the watering plan is adjustedbased on the determined vegetation health and soil conditions. Forexample, if, as discussed above, root depth increases with kitapplication, the watering plan may account for this increased root depthby increasing watering volume for each watering event. As anotherexample, if, as discussed above, the soil water depletion rate decreaseswith kit application, the watering plan may account for this decreasedwater depletion rate by increasing training time between watering eventsand therefore reducing the frequency of watering events.

After operation 318, the method 300 proceeds to operation 320 and thecentral controller 104 stores the adjusted watering plan as the currentwatering plan. The central controller 104 may store this data withininternal or external memory components.

In an alternate embodiment, a user may apply additives to the soil as analternative to or in addition to the soil enhancement kit. For example,the additional or alternate additives applied by a user may beconsidered when determining the soil enhancement kit composition (e.g.,at operation 306) and whether to adjust the watering plan (e.g., atoperation 316). For example, a user may separately purchase fertilizerfrom a third party to apply to his or her yard. The user may input anysoil additives applied to the soil into the system. For example, theuser may manually enter additive(s) applied (e.g., fertilizeringredients based on a label or the product name), scan a barcode on theproduct, upload an image of the additive(s) (e.g., capture and upload animage of an ingredient label or the product name), and the like. In someexamples, the system may be linked to a third party database to gatheradditional information on the additives applied. For example, where theuser inputs the product name or scans a barcode, the system may gatheradditional information on the product components through the third partydatabase. In some embodiments, scanning a barcode or uploading an imageof an additive creates an event that signals to the system that anadditive was applied to the soil.

FIG. 8 is a diagram illustrating an example of a deep root ecosystemcreated by the system of FIG. 1. As shown, roots 352 have grown to adepth 358 within the soil 354. The roots 352 grow both down and out invarious directions, taking up a 3D volume within the soil 354. Water isallowed to deplete to the water depletion line 356. In other words,water above the water depletion line 356 is allowed to deplete from thesoil 354 until the water level reaches the water depletion line 356, atwhich point more water may be applied to the soil 354. As shown, thewater depletion line 356 is 50% the depth 358 of the roots 352; however,it is contemplated that the water depletion line 356 may be greater than50% the depth of the roots, such as, for example, between 60% and 70%the depth of the roots 352. The water depletion line 356 is at ashallower depth within the soil 354 than the wilting point 362. Thewilting point 362 occurs when too much water is allowed to deplete,depriving the roots of water and resulting in wilting.

As shown in FIG. 8, a three dimensional volume surrounding a lowerportion of the roots 352 forms a deep root ecosystem 360 around theroots 352. The deep root ecosystem 360 may include water and one or moresoil additives (e.g., components of a soil enhancement kit) applied tothe soil, as discussed above. For example, the deep root ecosystem 360may include one or more micronutrients, macronutrients, bacteria, fungi,plant hormones, and other bio stimulant or organic matter, in a desiredcombination (e.g., desirable based on the conditions of the soil and/orvegetation prior to applying the one or more additives). In severalembodiments, water makes the soil additives soluble and brings the soiladditives, including immobilized soil additives (e.g., immobilizednutrients), within the deep root ecosystem 360. In several embodiments,the plant roots 352 thrive within the deep root ecosystem 360,encouraging the roots 352 to grow longer (e.g., the root cells toelongate). In the depicted example, water depletion occurs above thewater depletion line 356, while root growth occurs below the waterdepletion line 356.

Providing Timely Consumable Delivery Based on Consumable DemandPredictions

In several embodiments, consumable demand may be forecasted and theforecasted demand may be tied to one or more logistics models for one ormore third party suppliers to provide timely delivery of theconsumables. For example, FIG. 9 is a flow chart illustrating a methodof generating consumable manufacturing and delivery schedules based onpredicted consumable characteristics over time. The method 370 beginswith operation 372 and landscape characteristics are determined.Landscape characteristics may include vegetation characteristics (e.g.,type/species, height, root depth, etc.), soil conditions, wateringschedules, location, weather, land use, land age, temperature, lightexposure, and the like. In several embodiments, the landscapecharacteristics may be historical data over time (e.g., historicalvegetation characteristics, soil conditions, watering schedules,weather, land use, temperature, light exposure, etc.), and the like.Landscape characteristics may be input by a user or by one or moresensors, and/or retrieved from a database.

After operation 372, the method 370 proceeds to operation 374 andpredicted vegetation growth over time is determined. In someembodiments, the predicted growth over time is determined based on theone or more landscape characteristics. For example, based on historicaldata over time, vegetation growth over one or more seasons can bepredicted. In some embodiments, the predicted growth over time may bedynamic and may be updated as actual (as opposed to historical)landscape characteristic data is collected and input into the system. Asone example, actual temperature data may be input into the system. Thesystem may compare the actual temperature data to the historicaltemperature data to determine whether the time period (e.g., the monthof July) is hotter or cooler than usual. The growth curve may beadjusted accordingly during that time period (e.g., the month of July)based on the actual recorded data. In some embodiments, a standardyield/growth curve may be retrieved from a database based on one or morelandscape characteristics (e.g., based on location, species,temperature, and the like).

After operation 374, the method 370 proceeds to operation 376 andconsumable characteristics are predicted based on the growth curve.Consumable characteristics may include consumable type and amount,application timing, frequency and duration. Based on the point in thevegetation growth cycle, different consumables may be needed. Forexample, if certain undesirable vegetation (e.g., weeds) is expected tohave increased growth at a particular time period in the growth season,a weed killer may be needed to eliminate such vegetation during thattime. As another example, if growth of the desired vegetation isexpected to decline, for example, due to predicted high temperatures anddry soil conditions, then a soil conditioner may be needed to improvevegetation growth.

After operation 376, the method 370 proceeds to operation 378 and asupplier logistics model is analyzed to determine supply and timing fromacquisition and/or generation to delivery. A supplier logistics modelmay include a supplier's manufacturing capacity (e.g., how manyconsumable units the supplier can produce at a given time), timing(e.g., for stocking inventory, manufacturing, packaging, delivery,etc.), supply, and the like.

After operation 378, the method 370 proceeds to operation 380 and amanufacturing and delivery schedule is generated based on the predictedconsumable characteristics and the supplier logistics model. Based onthe consumable characteristics and the supplier's logistics model, atime to begin the process of acquiring and/or generating the neededconsumables can be determined. As one example, based on the growthcurve, the system predicted that 12 gallons of consumable A are neededby July 15 and 12 gallons of consumable B are needed by July 29. Basedon the analysis of the supplier logistics model, the system determinedthat a supplier can only generate 6 gallons of consumable A at a time,and that it takes a supplier 1 week to acquire enough raw materials toproduce 6 gallons of consumable A, 1 week to generate 6 gallons ofconsumable A, 1 week to ferment 6 gallons of consumable A, and 1 week todeliver consumable A. In this example, for consumable B, the systemdetermined that the supplier can only generate 12 gallons of consumableB at a time, and that it takes the supplier 2 weeks to acquire the rawmaterials to produce 12 gallons of consumable B, 2 weeks to generate 12gallons of consumable B, and 2 weeks to deliver consumable B.

The system may have also determined that the supplier has no rawmaterials in stock to produce consumable A and enough raw materials instock to produce 12 gallons of consumable B. Based on the logisticsmodel, to provide 12 gallons of consumable A by July 15, the supplierneeds to allow 1 week to deliver consumable A, 2 weeks to ferment 12gallons of consumable A, 2 weeks to generate 12 gallons of consumable A,and 2 weeks to acquire the raw materials for the 12 gallons ofconsumable A. In other words, the supplier needs 7 weeks fromacquisition to delivery of consumable A. Thus, to deliver by July 15,the supplier needs to begin the acquisition process 7 weeks prior (e.g.,around June 27). To provide 12 gallons of consumable B by July 29, thesupplier needs to allow 2 weeks to deliver consumable B and 2 weeks togenerate 12 gallons of consumable B. In this example, the supplier doesnot need to allow for acquisition time since the supplier already hasenough raw materials in stock to produce the 12 gallons of consumable B.In this example, the supplier needs 4 weeks from generation to deliveryof consumable B. Thus, to deliver by July 29, the supplier needs tobegin generating consumable B 4 weeks prior (e.g., around July 1).

In this manner, a supplier can prepare expected consumable orders inadvance to ensure consumable delivery at an optimal time for consumableapplication, improving the efficiency of consumable delivery.

Machine Learning-Based Schedule and/or Kit Adjustments

In several embodiments, the watering schedule and/or the soilenhancement kit may be established, adjusted, and/or implemented bymachine learning (ML). The amount of water applied, frequency ofwatering events, duration of watering, and the like, may depend onseveral factors (e.g., dynamic and/or static variables). For example,the watering schedule may depend on root depth, vegetation species, cropcoefficient (Ks), weather, available soil water capacity, maximumallowed water depletion, historical data, and the like. As one or moreof the variables changes over time, the watering schedule is adjusted tooptimize the vegetation health. To determine how to adjust the wateringschedule as the input variables change, the input variables may be fedinto a machine learning algorithm or model. In some examples, estimatedinput variables (e.g., historical, expected, common, or arbitraryvalues) are input into the model to determine an impact on the wateringschedule. In this example, several simulations with varying inputvariables may be run through the model so that the system can learn andimprove. In some examples, the input variables may be actual datacollected from users. For example, a user or the system (e.g., via asensor) may collect data on vegetation characteristics, soil condition,and the like, which is input into a machine learning algorithm todetermine a watering schedule.

As another example, the type, amount, time and duration of application,and the like, of the components/consumables in the soil enhancement kitmay vary depending on numerous variables, such as those discussed abovewith respect to the soil enhancement kit (e.g., vegetation data, soildata, zone data, etc.). As these variables change, the kit recipe (e.g.,the type and amount of consumables) and the kit application instructions(e.g., how and when to apply the consumables) may be adjusted tooptimize the vegetation health. To determine how to adjust the kitrecipe and kit application instructions as the input variables change,the input variables may be fed into a machine learning algorithm. Insome examples, estimated input variables are input into the algorithm todetermine the impact on the kit. In some examples, actual data collected(e.g., by a user or the system) may be input into the algorithm.

In some embodiments, the system learns how to improve vegetation healththrough reinforcement learning. As one example, FIG. 10 is a flow chartillustrating a method for generating a watering schedule or consumablerecipe (i.e., a soil enhancement kit) with reinforcement learning. Themethod 400 begins with operation 402 and input data is received by aserver. The input data may be one or more of the input variablesdiscussed previously, such as, for example, root depth, historicalwatering schedules, vegetation characteristics, soil conditions, zonedata, weather data, and environmental data. As one example, the inputdata may include root depth, crop coefficient (K_(c)), weather data,soil water capacity, maximum allowed water depletion, and the like. Someof the input variables may be dynamic (e.g., root depth), while othersmay be static (e.g., vegetation type). The input data may be generated(e.g., by various simulation runs testing different permutations andcombinations of data), estimated (e.g., arbitrary values, expectedvalues, historical values, etc.), or actual data collected.

After operation 402, the method 400 proceeds to operation 404 and amachine learning (ML) model is generated based on the one or more inputvariables. For example, one or more input variables may be fed into amachine learning algorithm, such as, for example a neural network,decision tree, support vector machine, and the like. As one example, thesystem may use statistical probability (e.g., sorting, weighting, andorganizing the inputs in varying permutations and combinations) todetermine a ML model. The inputs are sorted, weighted, and organizedaccording to the ML model to assess watering and/or consumable needs,amounts, frequency, duration, and the like.

After operation 404, the method 400 optionally proceeds to operation 406and a consumable recipe may be generated based on the ML model. The MLmodel may sort, weight, and organize the input data to assess consumableneeds (e.g., type, amounts, timing, frequency and/or duration ofapplication, and the like). For example, the ML model may usevegetation, soil, and zone data generated, estimated, or collected, topredict a consumable recipe and application instructions. The consumablerecipe may be transmitted to the server and/or to one or more thirdparty suppliers to obtain the needed consumables (e.g., type and amount)in an appropriate time frame according to the application instructions(e.g., time and duration of application).

In addition to operation 406 or as an alternative to operation 406, themethod 400 proceeds from operation 404 to operation 408 and a wateringschedule is generated based on the ML model. In this step, the ML modelmay sort, weight, and organize the input data to assess watering needs(e.g., watering volume, duration, frequency, etc.). For example, the MLmodel may use root depth, vegetation species, crop coefficient (K_(c)),weather, available soil water capacity, and maximum allowed waterdepletion to predict a watering schedule. The watering schedule may betransmitted to one or more controllers to open one or more sprinklervalves based on the watering schedule.

After operation 408, and optionally after operation 406, the method 400proceeds to operation 410 and feedback is received. Feedback may bereceived from a user or from the system (e.g., from one or moresensors). For example, a user may input feedback through an applicationon a user device that is connected to the server over the network. Asone example, a user may select a button (e.g., YES or NO), icon (e.g., athumbs up or thumbs down), or the like, on an application interface onthe user device. As another example, a user may key in feedback (e.g.,key in “approve” or “disapprove”) on the user device. The user mayprovide qualitative feedback (e.g., positive or negative, like ordislike, yes or no, thumbs up or down, or the like), quantitativefeedback (e.g., a number in a range or scale of numbers indicating alevel of like/approval or dislike/disapproval—e.g., between 1 and 10, 1being strong dislike/disapproval and 10 being strong like/approval), orthe like. In some cases, the feedback may be dynamic, such that the usermay input varying feedback over time.

User feedback may be personalized or aggregated feedback from multipleusers. For example, a user may input personalized feedback related tothe user's satisfaction with the state of the vegetation. For example,an eco-friendly user may prefer a less lush/green lawn and may provideapproval/satisfaction feedback with a lawn that is less lush/green,while a user preferring a lusher/greener lawn may providedisapproval/dissatisfaction feedback with the less lush/green lawn. Inthis example, the input is individualized based on the user. As anotherexample, user feedback may be collected from a plurality of users andinput into the system as global user feedback. In some examples, theplurality of users have one or more shared landscape or usercharacteristics. For example, the landscapes may be in the same orsimilar geographical location, may have the same or similar vegetationtype, may have similar characteristics (e.g., soil type, sun exposure,slope, etc.), may have the same watering restrictions (e.g., issued by amunicipality), and the like. As another example, the users may havesimilar behaviors or preferences for their lawns. For example, users mayuse a portion of the lawn more heavily than other areas (e.g., backyardused more than front yard), may have similar cost concerns (e.g., desireto spend less on a water bill), may have similar lawn appearancepreferences (e.g., eco-friendly vs. lush), may have similar travelschedules (e.g., weekend travelers), and the like. In this example,trends in the feedback data may be analyzed and an overall (e.g.,typical or average) user feedback may be determined. For example, if tenusers in the same geographical area provide positive feedback, whileonly one user provides negative feedback, the system may determine theoverall user feedback is positive.

As another example, feedback may be received by one or more sensors. Asone example, one or more cameras may be placed in the landscape and maycapture image data. The image data may be sent to the system 100 forimage processing. The system 100 may process the image to determinevegetation color, chlorophyll content, height, root depth, soildensity/porosity, and the like. As another example, other sensors 106may be used to monitor other characteristics, such as soil moisturelevels, nutrient levels, microbial activity, and the like. The system100 may use the collected data to determine overall landscape health.For example, greener and higher vegetation with greater chlorophyllcontent and a longer root depth may indicate overall healthy landscape,while browner, shorter vegetation with less chlorophyll content andshorter root depth may indicate overall unhealthy landscape.

After operation 410, the method 400 proceeds to operation 412 and theserver determines whether the feedback is positive. For example,positive feedback encourages a reinforcement ML model to continue thesame or similar path (e.g., reinforces the behavior of the model). Insuch a model, negative feedback causes the model to change course andalter its behavior to receive positive feedback (e.g., the model changesuntil the model learns it is on the right track). For example, positivefeedback from a user may include agreement (e.g., YES), approval,liking, a numerical value or range representing approval (e.g., 10 outof 10, a number between 6-10 out of 10), an icon representing approval(e.g., a thumbs up, a smiley face, clapping, etc.), or the like. Asanother example, positive feedback from the system (e.g., from a sensor)may include indications of a healthy landscape (e.g., greener and highervegetation, greater chlorophyll content, longer root depth, etc.).

If the feedback is positive, the method 400 proceeds to operation 414and the server continues to use the ML model for future wateringschedules and/or consumable recipes. In other words, the positivefeedback may reinforce the current ML model. The current ML model may beused by the same user or by other users having a similar landscape(e.g., in a same geographical location, with the same vegetation type,etc.). As one example, the ML model may be applied to a different zoneor property to determine a watering schedule and/or consumables recipefor that zone or property. In some cases, the ML model may be generatedfor a single zone or property having particular input variables (e.g., aparticular type of vegetation, root depth, K_(c), soil conditions,etc.). The zone or property may be representative of other zones orproperties (e.g., those having similar characteristics), such that theML model generated for the zone or property may be used as a baseline MLmodel for the other zones or properties. Based on a comparison of theother zones or properties to the representative zone or property,differences in zone or property characteristics may be determined. Thedifferences may be used to adjust the baseline ML model to createzone-specific or property-specific ML models.

If the feedback is negative, the method 400 proceeds to operation 416and the ML model is adjusted or a new ML model is generated. Forexample, if the ML model is adjusted, the manner in which the inputs aresorted, weighted, and/or organized may be tweaked to generate a wateringschedule that is different than the prior watering schedule generatedand transmitted at operation 408. Based on the negative feedbackprovided, the ML model is accordingly adjusted to improve the results(e.g., the vegetation health). In some examples, a new ML model isgenerated and a new manner of sorting, weighting, and/or organizing theinputs is applied to the new ML model. After operation 416, the method400 optionally proceeds to operation 406 and a new consumable recipe maybe generated based on the adjusted or new ML model. In addition tooperation 406 or as an alternative to operation 406, the method 400proceeds to operation 408 and a new watering schedule is generated basedon the adjusted or new ML model. The method 400 continues to proceedthrough the operations, as discussed above, until positive feedbackreinforces the generated ML model.

In some embodiments, the system learns how to improve vegetation healththrough supervised learning. As one example, FIG. 11 is a flow chartillustrating a method for generating a watering schedule or consumablerecipe (i.e., a soil enhancement kit) with supervised learning. Themethod 450 begins with operation 452 and input data is received. Theinput data may be the same as the input data discussed above withrespect to method 400 and operation 402.

After operation 452, the method 450 proceeds to operation 454 anddesired vegetation health data is received. For example, a user mayinput a desired vegetation coloration, chlorophyll content, or othermeasurable vegetation characteristic indicative of vegetation health. Insome examples, the desired vegetation health data is personalized data,specific to the user, or may be global data associated with multipleusers. For example, a plurality of users may input desirable conditionsfor vegetation health. In some examples, the plurality of users have oneor more shared landscape or user characteristics. For example, thelandscapes may be in the same or similar geographical location, may havethe same or similar vegetation type, may have similar characteristics(e.g., soil type, sun exposure, slope, etc.), may have the same wateringrestrictions (e.g., issued by a municipality), and the like. As anotherexample, the users may have similar behaviors or preferences for theirlawns. For example, users may use a portion of the lawn more heavilythan other areas (e.g., backyard used more than front yard), may havesimilar cost concerns (e.g., desire to spend less on a water bill), mayhave similar lawn appearance preferences (e.g., eco-friendly vs. lush),may have similar travel schedules (e.g., weekend travelers), and thelike. In this example, trends in the desirable vegetation health datamay be analyzed and an overall (e.g., typical or average) desiredvegetation health may be determined.

After operation 454, the method 450 proceeds to operation 456 and a MLmodel is generated based on the input data received at operation 452 andthe desired vegetation health data received or determined at operation454. For example, in supervised learning, the input variable(s) andoutput variable(s) are known and fed into a machine learning algorithmto learn the mapping function from the input to the output. In thisexample, the input data from operation 452 and vegetation health datafrom operation 454 are fed into a machine learning algorithm to generatethe ML model.

After operation 456, the method 450 proceeds to operation 458 andcurrent input data is received. For example, current input data may beinput by a user or by the system (e.g., by one or more sensors). Thecurrent input data may be current measurements of one or more of theinput variables discussed previously, such as, for example, root depth,vegetation characteristics, soil conditions, zone data, weather data,and environmental data.

After operation 458, the method 450 proceeds to operation 460 and thecurrent input variables are run through the ML model. For example, theinput variables may be sorted, weighted, mapped, and/or organizedaccording to the ML model to produce the desired vegetation health datareceived or determined at operation 454 (i.e., the desired output).

After operation 460, the method 450 proceeds to operation 462 andfeedback on the actual resulting vegetation health is received. Forexample, a user or the system (e.g., one or more sensors) may providefeedback on vegetation health. For example, the feedback may be one ormore of the types of feedback discussed for operation 410 of method 400.

After operation 462, the method 450 proceeds to operation 464 and thesystem determines whether the desired vegetation health is achieved. Forexample, the server may compare the resulting vegetation healthdetermined at operation 462 to the desired vegetation health datareceived or determined at operation 454 to determine whether the desiredvegetation health is achieved. If the desired vegetation health isachieved (e.g., the resulting vegetation health is the same as thedesired vegetation health), then the method 450 proceeds to operation468 and the system continues to use the ML model generated at operation456. If the desired vegetation health is not achieved (e.g., theresulting vegetation health differs from the desired vegetation health),then the method 450 proceeds to operation 466 and the ML model generatedat operation 456 is adjusted. For example, the sorting, weighting,mapping, and/or organization of the input variables may be tweaked toalter the ML model. After the ML model is adjusted at operation 466,current input data is received at operation 458 and run through theadjusted ML model at operation 460. The method 450 continues to proceedthrough the operations, as discussed above, until the desired vegetationhealth is achieved.

Additional System Components

FIG. 12 is a simplified block diagram of a computing device 250 that canbe used by one or more components of the system 100. For example, thecentral controller 104, user devices 108 a-108 n, and/or controllers102, 112 may include one or more of the components shown in FIG. 12 andbe used to execute one or more of the operations disclosed in methods150, 200, and 220. With reference to FIG. 12, the computing device 250may include one or more processing elements 252, an input/output (I/O)interface 254, a display 256, one or more memory components 258, anetwork interface 260, and one or more external devices 262. Each of thevarious components may be in communication with one another through oneor more busses, wireless means, or the like.

The processing element 252 is any type of electronic device capable ofprocessing, receiving, and/or transmitting instructions. For example,the processing element 252 may be a central processing unit,microprocessor, processor, or microcontroller. Additionally, it shouldbe noted that select components of the computer 250 may be controlled bya first processor and other components may be controlled by a secondprocessor, where the first and second processors may or may not be incommunication with each other.

The memory components 258 are used by the computer 250 to storeinstructions for the processing element 252, as well as store data, suchas the historical data, the vegetation and soil specifications, and thelike. The memory components 258 may be, for example, magneto-opticalstorage, read-only memory, random access memory, erasable programmablememory, flash memory, or a combination of one or more types of memorycomponents.

The display 256 provides visual feedback to a user and, optionally, canact as an input element to enable a user to control, manipulate, andcalibrate various components of the computing device 250. The display256 may be a liquid crystal display, plasma display, organiclight-emitting diode display, and/or cathode ray tube display. Inembodiments where the display 256 is used as an input, the display mayinclude one or more touch or input sensors, such as capacitive touchsensors, resistive grid, or the like.

The I/O interface 254 allows a user to enter data into the computer 250,as well as provides an input/output for the computer 250 to communicatewith other devices (e.g., central controller 104, controllers 102, 112,other computers, speakers, etc.). The I/O interface 254 can include oneor more input buttons, touch pads, and so on.

The network interface 260 provides communication to and from thecomputer 250 to other devices. For example, the network interface 260allows the server 104 to communicate with the controllers 102, 112through the network 114. The network interface 260 includes one or morecommunication protocols, such as, but not limited to, WiFi, Ethernet,Bluetooth, and so on. The network interface 260 may also include one ormore hardwired components, such as a Universal Serial Bus (USB) cable,or the like. The configuration of the network interface 260 depends onthe types of communication desired and may be modified to communicatevia WiFi, Bluetooth, and so on.

The external devices 262 are one or more devices that can be used toprovide various inputs to the computing device 250, e.g., mouse,microphone, keyboard, trackpad, or the like. The external devices 262may be local or remote and may vary as desired.

The above specification, examples and data provide a completedescription of the system and use of exemplary embodiments of theinvention as defined in the claims. Although various embodiments of theclaimed invention have been described above with a certain degree ofparticularity, or with reference to one or more individual embodiments,those skilled in the art could make numerous alterations to thedisclosed embodiments without departing from the spirit or scope of theclaimed invention. Other embodiments are therefore contemplated. It isintended that all matter contained in the above description and shown inthe accompanying drawings shall be interpreted as illustrative only ofparticular embodiments and not limiting. Changes in detail or structuremay be made without departing from the basic elements of the inventionas defined in the following claims. Further, it should be understoodthat logical operations may be performed in any order, unless explicitlyclaimed otherwise or a specific order is inherently necessitated by theclaim language.

What is claimed is:
 1. A method for generating a dynamic watering planthat reduces water consumption requirements for vegetation comprising:estimating, by a processing element, root depth of vegetation watered bya watering system; determining, by the processing element, an allowedwater depletion threshold of the vegetation based on the root depth;determining, by the processing element, a training watering plan toincrease the root depth of the vegetation over time based on the rootdepth and the allowed water depletion threshold; and transmitting thetraining watering plan to a flow controller for execution by thewatering system.
 2. The method of claim 1, wherein the training wateringplan adjusts watering volume per watering event based on changes in rootdepth over time.
 3. The method of claim 1, wherein the training wateringplan increases time periods between watering events as the allowed waterdepletion threshold increases based on increasing root depth.
 4. Themethod of claim 2, wherein the watering volume for a given wateringevent is sufficient to engulf at least a lower portion of the roots. 5.The method of claim 1, wherein determining the training watering planfurther comprises utilizing, by the processing element, at least one ofcrop coefficient and yield curve of the vegetation to determine thetraining watering plan.
 6. The method of claim 1, wherein determiningthe training watering plan further comprises utilizing, by theprocessing element, soil characteristics to determine the trainingwatering plan, the soil characteristic comprising at least one of watercontent, water capacity, water depletion rate, and temperature.
 7. Themethod of claim 1, wherein the training watering plan decreases wateringfrequency over time until a desired root depth is achieved.
 8. Themethod of claim 7, wherein the desired root depth is determined based onuser feedback corresponding to vegetative characteristics.
 9. The methodof claim 1, further comprising varying at least one of the root depthand the allowed water depletion threshold based on application of a soiladditive.
 10. A watering system for vegetation comprising: a serverconfigured to receive, process, and transmit information; a sensor fordetecting at least one of weather variables, soil moisture levels, orvegetation characteristics; one or more databases containing informationon at least one of watering history and vegetation specifications andcommunicatively coupled to the server; and one or more controllers incommunication with the server and connected to at least one water outletof a plurality of water outlets, the server including a non-transitorycomputer readable media and configured to execute instructions stored onthe non-transitory computer readable media, the instructions comprising:estimating a root depth value based on at least one of the weathervariables, soil moisture levels, vegetation characteristics, wateringhistory, and vegetation specifications; determining a water depletionthreshold based on the root depth value; estimating a water depletionrate based on at least one of the weather variables, soil moisturelevels, vegetation characteristics, watering history, and vegetationspecifications; and determining a watering plan to increase the rootdepth value based at least on the root depth value, the water depletionthreshold, and the water depletion rate.
 11. The watering system ofclaim 10, wherein the watering plan comprises: a plurality of wateringevents, wherein each watering event comprises a watering duration and awater volume, wherein the watering duration and the water volume aredetermined based on the root depth value; and a plurality of trainingperiods, wherein each training period is implemented between wateringevents and each training period is determined based on the waterdepletion threshold and the water depletion rate.
 12. The wateringsystem of claim 11, wherein the plurality of training periods increasein length as the watering plan continues to execute.
 13. The wateringsystem of claim 10, further comprising varying at least one of the rootdepth, the water depletion threshold, and the water depletion rate basedon application of a soil additive.
 14. The watering system of claim 10,wherein the instructions further comprise adjusting the watering planbased on user feedback.
 15. The watering system of claim 10, whereindetermining the watering plan comprises feeding at least the root depthvalue, the water depletion threshold, and the water depletion rate intoa machine learning model.
 16. The watering system of claim 15, whereinthe machine learning model is a reinforcement model, wherein positivefeedback reinforces the machine learning model.
 17. The watering systemof claim 10, wherein the sensor is a camera, and the server is furtherconfigured to process images received from the camera to determine atleast one of soil moisture levels and vegetation characteristics. 18.The watering system of claim 10, wherein an output signal is sent to theserver when the sensor is activated.
 19. A method for improvinglandscape health of an area, the method comprising: receiving, by aprocessor, landscape health data specific to the area, wherein thelandscape health data comprises vegetation and soil data, the vegetationdata comprising an estimated current root depth of the vegetationgrowing in the area; generating, by the processor, a recipe for a soilenhancement kit based on the landscape health data, wherein the recipeincludes at least one soil additive for increasing the estimated currentroot depth of the vegetation; determining, by the processor,instructions for applying the soil enhancement kit to the area, whereinthe instructions include information on timing, frequency, and durationof kit application; and transmitting, by the processor, the at least onesoil additive and instructions to a customer.
 20. The method of claim19, further comprising determining, by the processor, a time to ship thekit to the customer based on the landscape health, the timeframe for kitapplication, and a lifespan of the kit.
 21. The method of claim 19,further comprising determining, by the processor, adjustments to awatering schedule based on estimated changes in root depth due toapplication of the soil enhancement kit.
 22. The method of claim 19,wherein the at least one soil additive is at least one of a nutrient, amicrobe, a soil conditioner, a weed control, a wetting agent, and a biostimulant.
 23. The method of claim 19, wherein generating the recipe forthe soil enhancement kit comprises feeding the landscape health datainto a machine learning model.
 24. The method of claim 19, wherein thelandscape health data further comprises zone data corresponding to oneor more watering zones.
 25. A method of generating a consumablemanufacturing and delivery schedule, comprising: determining, by aprocessor, vegetation growth over time based on one or more landscapecharacteristics; estimating, by the processor, consumablecharacteristics based on the vegetation growth over time, wherein theconsumable characteristics comprise a consumable type and amount and atiming of application; analyzing, by the processor, a supplier logisticsmodel to determine consumable supply and timing from acquisition orgeneration of the consumable to delivery; generating, by the processor,a manufacturing and delivery schedule based on the estimated consumablecharacteristics and the supplier logistics model; and utilizing, by theprocessor, the manufacturing and delivery schedule to coordinatedelivery of the consumable to a user.